diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
index e5d4f5807..476a329bf 100644
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -133,6 +133,8 @@
     title: Pelican SVG
   - local: environments/sophistry_bench_sprint
     title: Sophistry Bench Sprint
+  - local: environments/latex_ocr
+    title: LaTeX OCR
   title: Environments
 - isExpanded: false
   sections:
diff --git a/docs/source/environments/latex_ocr.md b/docs/source/environments/latex_ocr.md
new file mode 100644
index 000000000..1fe8faae6
--- /dev/null
+++ b/docs/source/environments/latex_ocr.md
@@ -0,0 +1,79 @@
+<!-- openenv-source: latex_ocr_env -->
+# LaTeX OCR Environment
+
+A dataset-backed, single-step (bandit) RL environment for **image → LaTeX**
+transcription, served through OpenEnv.
+
+- **Task**: the agent is shown an image of a math/text expression and must
+  return its LaTeX source.
+- **Dataset**: tasks are served from a Hugging Face dataset (default
+  [`unsloth/LaTeX_OCR`](https://huggingface.co/datasets/unsloth/LaTeX_OCR):
+  `image` + `text` columns, `train`/`test` splits) via the OpenEnv **Task API**.
+- **Reward**: `[(1 - exact_weight) * (1 - CER) + exact_weight * exact_match] * length_factor`,
+  where `CER` is the normalized character edit distance over whitespace-stripped
+  LaTeX. With the default `exact_weight=0.4` (tunable via `LATEX_OCR_EXACT_WEIGHT`),
+  partial answers score in `[0, 0.6]`; only an exact match reaches `1.0`.
+  Computed **server-side** against the hidden ground truth — the agent never
+  sees the target on `reset`. Dense and smooth, for stable RL training.
+- **Length guard**: because the score is whitespace-insensitive, a policy could
+  otherwise emit the correct answer followed by unlimited whitespace and still
+  score `1.0` — a reward hack that, in RL, shows up as completions drifting to
+  the generation-length cap. `length_factor` decays the reward once the *raw*
+  prediction grows past `LATEX_OCR_OVERLONG_RATIO`× the target length (default
+  `4.0`, floor `LATEX_OCR_OVERLONG_FLOOR=80` chars); normal LaTeX spacing is well
+  within the allowance. Set `LATEX_OCR_OVERLONG_RATIO=0` to disable. The rubric
+  also strips code fences / `$`-delimiters from raw completions itself.
+
+## Episode
+
+```
+reset(split="test", index=0)  -> observation { image_base64, prompt, ... }   # target hidden
+step(LatexOCRAction(latex=…)) -> reward, done=True { target_latex, exact_match, char_error_rate }
+```
+
+## Task API
+
+| Endpoint | Purpose |
+|---|---|
+| `GET  /latex_ocr_env/splits` | list splits (`train`, `test`) |
+| `POST /latex_ocr_env/num_tasks` | row count for a split |
+| `POST /latex_ocr_env/tasks` | all task specs for a split |
+| `POST /latex_ocr_env/task` | one task by `{split, index}` |
+| `POST /latex_ocr_env/task_range` | slice `{split, start, stop}` |
+
+Client helpers: `env.list_splits()`, `env.num_tasks(split)`,
+`env.get_task(split, index)`, `env.get_task_range(split, start, stop)`.
+
+## Run locally
+
+```bash
+# From the repo root. LATEX_OCR_SPLITS=test avoids the 380MB train download.
+PYTHONPATH=src:envs/latex_ocr_env LATEX_OCR_SPLITS=test \
+  uv run --with datasets --with pillow --with fastapi --with uvicorn --with websockets \
+  uvicorn server.app:app --host 0.0.0.0 --port 8000
+```
+
+Then drive it (needs `HF_TOKEN` for the real VLM policy):
+
+```bash
+HF_TOKEN=hf_xxx PYTHONPATH=src \
+  uv run --with datasets --with pillow --with requests --with websockets --with openai \
+  python envs/latex_ocr_env/validate.py --split test --num 3 \
+  --model Qwen/Qwen2.5-VL-7B-Instruct
+```
+
+## Configuration (env vars)
+
+| Var | Default | Meaning |
+|---|---|---|
+| `LATEX_OCR_DATASET` | `unsloth/LaTeX_OCR` | source dataset |
+| `LATEX_OCR_IMAGE_COLUMN` | `image` | image column |
+| `LATEX_OCR_TEXT_COLUMN` | `text` | ground-truth LaTeX column |
+| `LATEX_OCR_SPLITS` | `train,test` | splits to expose |
+| `LATEX_OCR_MAX_ROWS` | — | cap rows per split (dev) |
+| `LATEX_OCR_EXACT_WEIGHT` | `0.4` | exact-match share of the reward |
+| `LATEX_OCR_OVERLONG_RATIO` | `4.0` | raw length allowed as a multiple of the target before the reward decays (`0` disables the length guard) |
+| `LATEX_OCR_OVERLONG_FLOOR` | `80` | minimum allowed raw length (chars) for short targets |
+
+Swap in any `(image, latex)` dataset by pointing `LATEX_OCR_DATASET` at it (and
+the column vars if they differ).
diff --git a/envs/latex_ocr_env/README.md b/envs/latex_ocr_env/README.md
new file mode 100644
index 000000000..19b153179
--- /dev/null
+++ b/envs/latex_ocr_env/README.md
@@ -0,0 +1,88 @@
+---
+title: LaTeX OCR Env
+emoji: 📐
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_port: 8000
+pinned: false
+---
+
+# LaTeX OCR Environment
+
+A dataset-backed, single-step (bandit) RL environment for **image → LaTeX**
+transcription, served through OpenEnv.
+
+- **Task**: the agent is shown an image of a math/text expression and must
+  return its LaTeX source.
+- **Dataset**: tasks are served from a Hugging Face dataset (default
+  [`unsloth/LaTeX_OCR`](https://huggingface.co/datasets/unsloth/LaTeX_OCR):
+  `image` + `text` columns, `train`/`test` splits) via the OpenEnv **Task API**.
+- **Reward**: `[(1 - exact_weight) * (1 - CER) + exact_weight * exact_match] * length_factor`,
+  where `CER` is the normalized character edit distance over whitespace-stripped
+  LaTeX. With the default `exact_weight=0.4` (tunable via `LATEX_OCR_EXACT_WEIGHT`),
+  partial answers score in `[0, 0.6]`; only an exact match reaches `1.0`.
+  Computed **server-side** against the hidden ground truth — the agent never
+  sees the target on `reset`. Dense and smooth, for stable RL training.
+- **Length guard**: because the score is whitespace-insensitive, a policy could
+  otherwise emit the correct answer followed by unlimited whitespace and still
+  score `1.0` — a reward hack that, in RL, shows up as completions drifting to
+  the generation-length cap. `length_factor` decays the reward once the *raw*
+  prediction grows past `LATEX_OCR_OVERLONG_RATIO`× the target length (default
+  `4.0`, floor `LATEX_OCR_OVERLONG_FLOOR=80` chars); normal LaTeX spacing is well
+  within the allowance. Set `LATEX_OCR_OVERLONG_RATIO=0` to disable. The rubric
+  also strips code fences / `$`-delimiters from raw completions itself.
+
+## Episode
+
+```
+reset(split="test", index=0)  -> observation { image_base64, prompt, ... }   # target hidden
+step(LatexOCRAction(latex=…)) -> reward, done=True { target_latex, exact_match, char_error_rate }
+```
+
+## Task API
+
+| Endpoint | Purpose |
+|---|---|
+| `GET  /latex_ocr_env/splits` | list splits (`train`, `test`) |
+| `POST /latex_ocr_env/num_tasks` | row count for a split |
+| `POST /latex_ocr_env/tasks` | all task specs for a split |
+| `POST /latex_ocr_env/task` | one task by `{split, index}` |
+| `POST /latex_ocr_env/task_range` | slice `{split, start, stop}` |
+
+Client helpers: `env.list_splits()`, `env.num_tasks(split)`,
+`env.get_task(split, index)`, `env.get_task_range(split, start, stop)`.
+
+## Run locally
+
+```bash
+# From the repo root. LATEX_OCR_SPLITS=test avoids the 380MB train download.
+PYTHONPATH=src:envs/latex_ocr_env LATEX_OCR_SPLITS=test \
+  uv run --with datasets --with pillow --with fastapi --with uvicorn --with websockets \
+  uvicorn server.app:app --host 0.0.0.0 --port 8000
+```
+
+Then drive it (needs `HF_TOKEN` for the real VLM policy):
+
+```bash
+HF_TOKEN=hf_xxx PYTHONPATH=src \
+  uv run --with datasets --with pillow --with requests --with websockets --with openai \
+  python envs/latex_ocr_env/validate.py --split test --num 3 \
+  --model Qwen/Qwen2.5-VL-7B-Instruct
+```
+
+## Configuration (env vars)
+
+| Var | Default | Meaning |
+|---|---|---|
+| `LATEX_OCR_DATASET` | `unsloth/LaTeX_OCR` | source dataset |
+| `LATEX_OCR_IMAGE_COLUMN` | `image` | image column |
+| `LATEX_OCR_TEXT_COLUMN` | `text` | ground-truth LaTeX column |
+| `LATEX_OCR_SPLITS` | `train,test` | splits to expose |
+| `LATEX_OCR_MAX_ROWS` | — | cap rows per split (dev) |
+| `LATEX_OCR_EXACT_WEIGHT` | `0.4` | exact-match share of the reward |
+| `LATEX_OCR_OVERLONG_RATIO` | `4.0` | raw length allowed as a multiple of the target before the reward decays (`0` disables the length guard) |
+| `LATEX_OCR_OVERLONG_FLOOR` | `80` | minimum allowed raw length (chars) for short targets |
+
+Swap in any `(image, latex)` dataset by pointing `LATEX_OCR_DATASET` at it (and
+the column vars if they differ).
diff --git a/envs/latex_ocr_env/__init__.py b/envs/latex_ocr_env/__init__.py
new file mode 100644
index 000000000..955982180
--- /dev/null
+++ b/envs/latex_ocr_env/__init__.py
@@ -0,0 +1,12 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""LaTeX OCR Environment - dataset-backed, single-step RL for image→LaTeX."""
+
+from .client import LatexOCREnv
+from .models import LatexOCRAction, LatexOCRObservation
+
+__all__ = ["LatexOCRAction", "LatexOCRObservation", "LatexOCREnv"]
diff --git a/envs/latex_ocr_env/client.py b/envs/latex_ocr_env/client.py
new file mode 100644
index 000000000..83e520385
--- /dev/null
+++ b/envs/latex_ocr_env/client.py
@@ -0,0 +1,137 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+LaTeX OCR Environment client.
+
+Gym-style client (reset/step over WebSocket) plus thin HTTP helpers for the
+Task API so a trainer can enumerate and select dataset tasks.
+
+Example:
+    >>> with LatexOCREnv(base_url="http://localhost:8000") as env:
+    ...     print(env.list_splits())            # ["train", "test"]
+    ...     print(env.num_tasks("test"))        # 7595
+    ...     result = env.reset(split="test", index=0)
+    ...     img = result.observation.image_base64
+    ...     # ... run a VLM to produce `latex` ...
+    ...     result = env.step(LatexOCRAction(latex=latex))
+    ...     print(result.reward, result.observation.target_latex)
+"""
+
+from __future__ import annotations
+
+from typing import Any, Optional
+from urllib.parse import urljoin
+
+import requests
+
+try:
+    from openenv.core.client_types import StepResult
+    from openenv.core.env_client import EnvClient
+    from openenv.core.env_server.types import State
+except ImportError:
+    from core.client_types import StepResult
+    from core.env_client import EnvClient
+    from core.env_server.types import State
+
+from .models import LatexOCRAction, LatexOCRObservation
+
+ENV_NAME = "latex_ocr_env"
+
+
+class LatexOCREnv(EnvClient[LatexOCRAction, LatexOCRObservation, State]):
+    """Client for the LaTeX OCR environment."""
+
+    def reset(
+        self,
+        split: str = "train",
+        index: Optional[int] = None,
+        seed: Optional[int] = None,
+        **kwargs: Any,
+    ) -> StepResult[LatexOCRObservation]:
+        """Reset to a specific dataset task (or a random one if ``index`` is None)."""
+        payload: dict[str, Any] = {"split": split}
+        if index is not None:
+            payload["index"] = index
+        if seed is not None:
+            payload["seed"] = seed
+        payload.update(kwargs)
+        return super().reset(**payload)
+
+    # --- Gym-style (de)serialization required by EnvClient ---
+    def _step_payload(self, action: LatexOCRAction) -> dict[str, Any]:
+        return action.model_dump()
+
+    def _parse_result(self, data: dict[str, Any]) -> StepResult[LatexOCRObservation]:
+        obs_data = dict(data.get("observation", data))
+        # Core serialization lifts reward/done to the top level and strips them from
+        # the observation payload; merge them back so observation.reward/.done match
+        # StepResult (consistent with other env clients).
+        reward = data.get("reward", obs_data.get("reward"))
+        done = data.get("done", obs_data.get("done"))
+        obs_data["reward"] = reward
+        obs_data["done"] = done
+        obs = LatexOCRObservation(**obs_data)
+        base = dict(observation=obs, reward=reward, done=done)
+        # Newer core's StepResult carries `info`; older core does not.
+        try:
+            return StepResult(**base, info=data.get("info", {}))
+        except TypeError:
+            return StepResult(**base)
+
+    def _parse_state(self, data: dict[str, Any]) -> State:
+        return State(**data)
+
+    # ------------------------------------------------------------------ #
+    # Task API (HTTP)                                                     #
+    # ------------------------------------------------------------------ #
+    def _http_base(self) -> str:
+        # Newer core exposes ``_base_url`` (http); older core stores only
+        # ``_ws_url`` (ws://host/ws). Derive an http base that works for both.
+        base = getattr(self, "_base_url", None)
+        if not base:
+            ws = getattr(self, "_ws_url", None)
+            if not ws:
+                raise RuntimeError("Task API requires an HTTP base URL")
+            base = ws[:-3] if ws.endswith("/ws") else ws
+        base = base.replace("wss://", "https://").replace("ws://", "http://")
+        return base if base.endswith("/") else base + "/"
+
+    def list_splits(self) -> list[str]:
+        resp = requests.get(
+            urljoin(self._http_base(), f"{ENV_NAME}/splits"), timeout=30
+        )
+        resp.raise_for_status()
+        return [s["name"] for s in resp.json()]
+
+    def num_tasks(self, split: str) -> int:
+        resp = requests.post(
+            urljoin(self._http_base(), f"{ENV_NAME}/num_tasks"),
+            json={"split": split},
+            timeout=60,
+        )
+        resp.raise_for_status()
+        return int(resp.json()["num_tasks"])
+
+    def get_task(self, split: str, index: int) -> dict[str, Any]:
+        resp = requests.post(
+            urljoin(self._http_base(), f"{ENV_NAME}/task"),
+            json={"split": split, "index": index},
+            timeout=60,
+        )
+        resp.raise_for_status()
+        return resp.json()["task"]
+
+    def get_task_range(
+        self, split: str, start: int | None = None, stop: int | None = None
+    ) -> list[dict[str, Any]]:
+        resp = requests.post(
+            urljoin(self._http_base(), f"{ENV_NAME}/task_range"),
+            json={"split": split, "start": start, "stop": stop},
+            timeout=120,
+        )
+        resp.raise_for_status()
+        return resp.json()["tasks"]
diff --git a/envs/latex_ocr_env/models.py b/envs/latex_ocr_env/models.py
new file mode 100644
index 000000000..390ea42b2
--- /dev/null
+++ b/envs/latex_ocr_env/models.py
@@ -0,0 +1,84 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Data models for the LaTeX OCR Environment.
+
+The environment presents a rendered math/text image (from a Hugging Face
+dataset) and the agent must return the LaTeX source that produced it. Reward
+is computed server-side against the hidden ground-truth LaTeX.
+"""
+
+from openenv.core.env_server.types import Action, Observation
+from pydantic import Field
+
+
+class LatexOCRAction(Action):
+    """Agent's transcription attempt for the current image.
+
+    A single ``latex`` string. Episodes are single-step (bandit): one action
+    per reset terminates the episode.
+    """
+
+    latex: str = Field(
+        default="",
+        description="Predicted LaTeX source for the current image.",
+    )
+
+
+class LatexOCRObservation(Observation):
+    """Observation for the LaTeX OCR environment.
+
+    On ``reset`` the observation carries the image the agent must transcribe;
+    ``target_latex`` is intentionally empty so the agent cannot cheat. On the
+    terminal ``step`` observation the ground truth and scoring details are
+    revealed for logging/training.
+    """
+
+    # --- Present on reset (the task prompt) ---
+    image_base64: str = Field(
+        default="",
+        description="Base64-encoded image (PNG) the agent must transcribe.",
+    )
+    image_format: str = Field(
+        default="png", description="Encoding format of image_base64."
+    )
+    prompt: str = Field(
+        default="",
+        description="Instruction shown to the agent describing the OCR task.",
+    )
+    split: str = Field(default="", description="Dataset split of the current task.")
+    index: int = Field(
+        default=-1, description="Row index (materialize) or cursor position (stream)."
+    )
+    task_id: str = Field(default="", description="Stable task identifier.")
+
+    # --- Streaming-mode progress (present when mode='stream') ---
+    total: int = Field(default=-1, description="Total rows in the split (denominator).")
+    remaining: int = Field(default=-1, description="Rows left in this stream.")
+    pct_done: float = Field(
+        default=0.0, description="Fraction of the split consumed so far."
+    )
+    exhausted: bool = Field(
+        default=False, description="True once the stream is fully consumed."
+    )
+
+    # --- Present after step (grading result) ---
+    predicted_latex: str = Field(
+        default="", description="The LaTeX the agent submitted."
+    )
+    target_latex: str = Field(
+        default="",
+        description="Ground-truth LaTeX. Empty until the episode terminates.",
+    )
+    exact_match: bool = Field(
+        default=False,
+        description="Whether the normalized prediction equals the target.",
+    )
+    char_error_rate: float = Field(
+        default=1.0,
+        description="Normalized Levenshtein distance between prediction and target.",
+    )
diff --git a/envs/latex_ocr_env/openenv.yaml b/envs/latex_ocr_env/openenv.yaml
new file mode 100644
index 000000000..6246914b5
--- /dev/null
+++ b/envs/latex_ocr_env/openenv.yaml
@@ -0,0 +1,41 @@
+spec_version: 1
+name: latex_ocr_env
+type: space
+runtime: fastapi
+app: server.app:app
+port: 8000
+
+description: >
+  Dataset-backed LaTeX OCR environment. Agents receive an image of a
+  mathematical expression and must return its LaTeX source. Reward is a blend
+  of normalized character edit distance and an exact-match bonus, computed
+  server-side against the hidden ground-truth LaTeX. Tasks are served from a
+  Hugging Face dataset (default: unsloth/LaTeX_OCR) via the OpenEnv Task API.
+version: "0.1.0"
+
+# Environment configuration defaults (overridable via env vars)
+environment:
+  dataset_name: unsloth/LaTeX_OCR      # LATEX_OCR_DATASET
+  image_column: image                  # LATEX_OCR_IMAGE_COLUMN
+  text_column: text                    # LATEX_OCR_TEXT_COLUMN
+  splits: "train,test"                 # LATEX_OCR_SPLITS
+  exact_weight: 0.4                    # LATEX_OCR_EXACT_WEIGHT (edit 0.6 / exact 0.4)
+  overlong_ratio: 4.0                  # LATEX_OCR_OVERLONG_RATIO (length guard; 0 disables)
+  overlong_floor: 80                   # LATEX_OCR_OVERLONG_FLOOR (min allowed raw chars)
+
+# Reward definition
+rubric:
+  name: LatexOCRRubric
+  formula: "[(1 - exact_weight) * (1 - CER) + exact_weight * exact_match] * length_factor"
+  cer: "normalized Levenshtein distance over whitespace-stripped LaTeX"
+  length_factor: "decays reward when the raw prediction exceeds overlong_ratio x target length (anti whitespace-padding hack)"
+
+# Task API (dataset discovery) — auto-exposed by the OpenEnv server
+task_api:
+  splits: [train, test]
+  endpoints:
+    - GET  /latex_ocr_env/splits
+    - POST /latex_ocr_env/num_tasks
+    - POST /latex_ocr_env/tasks
+    - POST /latex_ocr_env/task
+    - POST /latex_ocr_env/task_range
diff --git a/envs/latex_ocr_env/pyproject.toml b/envs/latex_ocr_env/pyproject.toml
new file mode 100644
index 000000000..be5288bfe
--- /dev/null
+++ b/envs/latex_ocr_env/pyproject.toml
@@ -0,0 +1,38 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+[build-system]
+requires = ["setuptools>=45", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "openenv-latex_ocr_env"
+version = "0.1.0"
+description = "LaTeX OCR environment for OpenEnv (image -> LaTeX transcription with edit-distance reward)"
+requires-python = ">=3.10"
+dependencies = [
+    # Core OpenEnv runtime (FastAPI server + Task API). 0.4.1 is the first
+    # release that ships the Task API (#726).
+    "openenv>=0.4.1",
+    "datasets>=4.0.0",
+    "pillow>=10.0.0",
+    "requests>=2.31.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=8.0.0",
+    # OpenAI SDK is only needed to drive the env with a VLM policy (validate.py).
+    "openai>=1.40.0",
+]
+
+[project.scripts]
+server = "latex_ocr_env.server.app:main"
+
+[tool.setuptools]
+include-package-data = true
+packages = ["latex_ocr_env", "latex_ocr_env.server"]
+package-dir = { "latex_ocr_env" = ".", "latex_ocr_env.server" = "server" }
diff --git a/envs/latex_ocr_env/server/Dockerfile b/envs/latex_ocr_env/server/Dockerfile
new file mode 100644
index 000000000..f3c179c3e
--- /dev/null
+++ b/envs/latex_ocr_env/server/Dockerfile
@@ -0,0 +1,59 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Multi-stage build using openenv-base. Works for in-repo and standalone builds.
+#
+# Build from the env directory:
+#   docker build -t latex-ocr-env:latest -f server/Dockerfile .
+# Run:
+#   docker run -p 8000:8000 latex-ocr-env:latest
+
+ARG BASE_IMAGE=ghcr.io/huggingface/openenv-base:latest
+FROM ${BASE_IMAGE} AS builder
+
+WORKDIR /app
+ARG BUILD_MODE=in-repo
+COPY . /app/env
+WORKDIR /app/env
+
+RUN if ! command -v uv >/dev/null 2>&1; then \
+        curl -LsSf https://astral.sh/uv/install.sh | sh && \
+        mv /root/.local/bin/uv /usr/local/bin/uv && \
+        mv /root/.local/bin/uvx /usr/local/bin/uvx; \
+    fi
+
+RUN apt-get update && apt-get install -y --no-install-recommends git \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ -f uv.lock ]; then \
+        uv sync --frozen --no-install-project --no-editable; \
+    else \
+        uv sync --no-install-project --no-editable; \
+    fi
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ -f uv.lock ]; then \
+        uv sync --frozen --no-editable; \
+    else \
+        uv sync --no-editable; \
+    fi
+
+FROM ${BASE_IMAGE}
+WORKDIR /app
+COPY --from=builder /app/env/.venv /app/.venv
+COPY --from=builder /app/env /app/env
+ENV PATH="/app/.venv/bin:$PATH"
+ENV PYTHONPATH="/app/env:$PYTHONPATH"
+ENV PYTHONUNBUFFERED=1
+
+# Pre-caching the dataset at build time is optional; by default it downloads on
+# first request. Set LATEX_OCR_SPLITS=test to limit to the small split.
+
+HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
+    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/healthz')" || exit 1
+
+CMD ["uvicorn", "latex_ocr_env.server.app:app", "--host", "0.0.0.0", "--port", "8000", "--ws-ping-interval", "120", "--ws-ping-timeout", "600"]
diff --git a/envs/latex_ocr_env/server/__init__.py b/envs/latex_ocr_env/server/__init__.py
new file mode 100644
index 000000000..850fe095c
--- /dev/null
+++ b/envs/latex_ocr_env/server/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Server package for the LaTeX OCR Environment."""
diff --git a/envs/latex_ocr_env/server/app.py b/envs/latex_ocr_env/server/app.py
new file mode 100644
index 000000000..475f3167a
--- /dev/null
+++ b/envs/latex_ocr_env/server/app.py
@@ -0,0 +1,61 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+FastAPI application for the LaTeX OCR Environment.
+
+Exposes LatexOCREnvironment over HTTP + WebSocket (Gym-style) endpoints and the
+Task API for dataset-backed task discovery.
+
+Usage:
+    # Development:
+    PYTHONPATH=. uvicorn server.app:app --host 0.0.0.0 --port 8000
+
+    # Or run directly:
+    python -m server.app
+"""
+
+import os
+
+try:
+    from openenv.core.env_server import create_app
+
+    from ..models import LatexOCRAction, LatexOCRObservation
+    from .gradio_ui import latex_ocr_ui_builder
+    from .latex_ocr_environment import LatexOCREnvironment
+except ImportError:  # standalone (uvicorn server.app:app)
+    from models import LatexOCRAction, LatexOCRObservation
+    from openenv.core.env_server import create_app
+    from server.gradio_ui import latex_ocr_ui_builder
+    from server.latex_ocr_environment import LatexOCREnvironment
+
+
+app = create_app(
+    LatexOCREnvironment,
+    LatexOCRAction,
+    LatexOCRObservation,
+    env_name="latex_ocr_env",
+    max_concurrent_envs=int(os.environ.get("LATEX_OCR_MAX_SESSIONS", "16")),
+    gradio_builder=latex_ocr_ui_builder,
+    custom_tab_name="Try it",
+    custom_tab_primary=True,
+)
+
+
+@app.get("/healthz")
+async def health() -> dict[str, str]:
+    """Lightweight service health endpoint for orchestration checks."""
+    return {"status": "ok"}
+
+
+def main() -> None:
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/envs/latex_ocr_env/server/gradio_ui.py b/envs/latex_ocr_env/server/gradio_ui.py
new file mode 100644
index 000000000..69c3cd949
--- /dev/null
+++ b/envs/latex_ocr_env/server/gradio_ui.py
@@ -0,0 +1,141 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Custom Gradio UI for the LaTeX OCR environment.
+
+Interactive playground:
+  1. "Get next task"  -> pulls a task (image) from the env; shows progress.
+  2. type LaTeX        -> the user's transcription attempt.
+  3. "Score"           -> grades it server-side and reveals the ground truth.
+
+Drives the *same* environment instance the API uses, via ``web_manager``, so in
+stream mode the cursor and progress here match the real episode flow.
+"""
+
+from __future__ import annotations
+
+import base64
+import io
+from typing import Any, Dict, List, Optional
+
+import gradio as gr
+
+
+def _decode_image(b64: str):
+    if not b64:
+        return None
+    try:
+        from PIL import Image
+
+        return Image.open(io.BytesIO(base64.b64decode(b64)))
+    except Exception:
+        return None
+
+
+def _obs(result: Dict[str, Any]) -> Dict[str, Any]:
+    """reset/step return {'observation': {...}, 'reward':..., 'done':...}."""
+    if isinstance(result, dict) and "observation" in result:
+        return result["observation"] or {}
+    return result or {}
+
+
+def latex_ocr_ui_builder(
+    web_manager,
+    action_fields: List[Dict[str, Any]],
+    metadata: Optional[Any],
+    is_chat_env: bool,
+    title: str,
+    quick_start_md: Optional[str],
+) -> gr.Blocks:
+    def _splits() -> List[str]:
+        try:
+            return web_manager.env.list_splits()
+        except Exception:
+            return ["train"]
+
+    def _progress_md(o: Dict[str, Any]) -> str:
+        total = o.get("total", -1)
+        if total and total > 0:
+            return (
+                f"**Task `{o.get('task_id', '')}`** · position "
+                f"**{o.get('index')}/{total}** "
+                f"({o.get('pct_done', 0):.4%}) · remaining **{o.get('remaining')}**"
+            )
+        return f"**Task `{o.get('task_id', '')}`** (index {o.get('index')})"
+
+    with gr.Blocks(title=f"{title} — Playground") as demo:
+        gr.Markdown(f"# {title} — try it")
+        gr.Markdown(
+            "Transcribe the image into **LaTeX**, then score it. Reward = "
+            "`0.8·(1−CER) + 0.2·exact_match` against the hidden ground truth."
+        )
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                split_dd = gr.Dropdown(
+                    choices=_splits(),
+                    value=(_splits() or ["train"])[0],
+                    label="Split",
+                    interactive=True,
+                )
+                get_btn = gr.Button("🎲 Get next task", variant="primary", size="lg")
+                progress_md = gr.Markdown("*No task yet — click “Get next task”.*")
+                image = gr.Image(label="Image to transcribe", type="pil", height=200)
+
+            with gr.Column(scale=1):
+                pred_box = gr.Textbox(
+                    label="Your LaTeX prediction",
+                    placeholder="e.g.  x ^ { 2 } + 1",
+                    lines=4,
+                )
+                score_btn = gr.Button("✅ Score", variant="primary", size="lg")
+                score_md = gr.Markdown("")
+                truth_box = gr.Textbox(
+                    label="Ground truth (revealed after scoring)",
+                    lines=4,
+                    interactive=False,
+                )
+
+        async def on_get_task(split: str):
+            result = await web_manager.reset_environment({"split": split})
+            o = _obs(result)
+            return (
+                _decode_image(o.get("image_base64", "")),
+                _progress_md(o),
+                "",  # clear prediction
+                "",  # clear score
+                "",  # clear truth
+            )
+
+        async def on_score(prediction: str):
+            try:
+                result = await web_manager.step_environment({"latex": prediction or ""})
+            except Exception as e:
+                return f"⚠️ {e}. Click **Get next task** first.", ""
+            o = _obs(result)
+            # The server serializes reward at the top level; fall back to the
+            # observation for older cores.
+            reward = result.get("reward") if isinstance(result, dict) else None
+            if reward is None:
+                reward = o.get("reward")
+            reward = reward if reward is not None else 0.0
+            mark = "🎯 **exact match**" if o.get("exact_match") else "≈ partial"
+            md = (
+                f"### Reward: `{reward:.4f}`  {mark}\n"
+                f"- character error rate: `{o.get('char_error_rate', 1.0):.4f}`\n"
+                f"- exact match: `{o.get('exact_match', False)}`"
+            )
+            return md, o.get("target_latex", "")
+
+        get_btn.click(
+            on_get_task,
+            inputs=[split_dd],
+            outputs=[image, progress_md, pred_box, score_md, truth_box],
+        )
+        score_btn.click(on_score, inputs=[pred_box], outputs=[score_md, truth_box])
+
+    return demo
diff --git a/envs/latex_ocr_env/server/latex_ocr_environment.py b/envs/latex_ocr_env/server/latex_ocr_environment.py
new file mode 100644
index 000000000..a3c54ce6b
--- /dev/null
+++ b/envs/latex_ocr_env/server/latex_ocr_environment.py
@@ -0,0 +1,379 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+LaTeX OCR Environment.
+
+A dataset-backed, single-step (bandit) RL environment over a Hugging Face
+dataset of (image, LaTeX) pairs. Reward is computed against the hidden
+ground-truth LaTeX via :class:`LatexOCRRubric`.
+
+Two access modes (env var ``LATEX_OCR_MODE``):
+
+- ``materialize`` (default) — the split is loaded and indexed. Supports the full
+  Task API with random access: ``reset(split, index)`` selects any row. Best
+  when the dataset fits on disk and is re-read across epochs.
+
+- ``stream`` — a sequential **cursor** over a streamed split (no full download).
+  ``reset()`` pulls the *next* sample; every observation carries progress
+  (``index``, ``total``, ``remaining``, ``pct_done``). No-repeat within a pass;
+  ``reset(index=i)`` random access is unsupported by design. Best for very large
+  / TB-scale datasets.
+
+The dataset (materialize) and the split row-count (stream) are cached at module
+scope because the server instantiates a fresh env per Task API call.
+"""
+
+from __future__ import annotations
+
+import base64
+import io
+import logging
+import os
+import random
+from functools import lru_cache
+from typing import Any, Optional
+from uuid import uuid4
+
+from openenv.core.env_server.interfaces import Environment
+from openenv.core.env_server.types import State
+from PIL import Image
+
+try:
+    from ..models import LatexOCRAction, LatexOCRObservation
+except ImportError:  # standalone (uvicorn server.app:app)
+    from models import LatexOCRAction, LatexOCRObservation
+
+from .rubric import LatexOCRRubric
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_DATASET = os.environ.get("LATEX_OCR_DATASET", "unsloth/LaTeX_OCR")
+IMAGE_COLUMN = os.environ.get("LATEX_OCR_IMAGE_COLUMN", "image")
+TEXT_COLUMN = os.environ.get("LATEX_OCR_TEXT_COLUMN", "text")
+DEFAULT_MODE = os.environ.get("LATEX_OCR_MODE", "materialize")  # materialize | stream
+SEED = int(os.environ.get("LATEX_OCR_SEED", "0"))
+SHUFFLE_BUFFER = int(os.environ.get("LATEX_OCR_SHUFFLE_BUFFER", "0"))
+CONFIG = os.environ.get("LATEX_OCR_CONFIG") or None
+DEFAULT_PROMPT = os.environ.get(
+    "LATEX_OCR_PROMPT",
+    "Transcribe the mathematical expression in this image into LaTeX. "
+    "Return only the LaTeX source, with no surrounding text or code fences.",
+)
+
+
+def _configured_splits() -> list[str]:
+    raw = os.environ.get("LATEX_OCR_SPLITS", "train,test")
+    return [s.strip() for s in raw.split(",") if s.strip()]
+
+
+def _max_rows() -> int | None:
+    """Per-split row cap (LATEX_OCR_MAX_ROWS), honored in both modes. None = no cap."""
+    v = os.environ.get("LATEX_OCR_MAX_ROWS")
+    return int(v) if v else None
+
+
+# Safety cap on get_task_range() stub generation in stream mode: the metadata
+# row-count can be enormous, so an unbounded range must not build a giant list.
+_STREAM_RANGE_CAP = int(os.environ.get("LATEX_OCR_STREAM_RANGE_CAP", "100000"))
+
+
+@lru_cache(maxsize=None)
+def _load_split(dataset_name: str, split: str):
+    """Load + cache a full split (materialize mode) for the process lifetime."""
+    from datasets import load_dataset
+
+    logger.info("Materializing %s split=%s ...", dataset_name, split)
+    ds = load_dataset(
+        dataset_name, name=CONFIG, split=split, token=os.environ.get("HF_TOKEN")
+    )
+    max_rows = os.environ.get("LATEX_OCR_MAX_ROWS")
+    if max_rows:
+        ds = ds.select(range(min(int(max_rows), len(ds))))
+    return ds
+
+
+@lru_cache(maxsize=None)
+def _split_total(dataset_name: str, split: str) -> int:
+    """Row count from dataset metadata — cheap, no data download (stream mode)."""
+    from datasets import load_dataset_builder
+
+    builder = load_dataset_builder(
+        dataset_name, name=CONFIG, token=os.environ.get("HF_TOKEN")
+    )
+    info = builder.info.splits.get(split)
+    if info is None or info.num_examples is None:
+        return -1
+    return int(info.num_examples)
+
+
+def _encode_image(image: Any) -> str:
+    # Normalize to PNG so the hard-coded image_format="png" is always correct,
+    # even when a swapped-in dataset stores raw (e.g. JPEG) bytes in its image column.
+    if isinstance(image, (bytes, bytearray)):
+        image = Image.open(io.BytesIO(bytes(image)))
+    buffer = io.BytesIO()
+    image.convert("RGB").save(buffer, format="PNG")
+    return base64.b64encode(buffer.getvalue()).decode("ascii")
+
+
+class LatexOCREnvironment(Environment):
+    """Single-step LaTeX OCR environment (materialize or streaming cursor)."""
+
+    SUPPORTS_CONCURRENT_SESSIONS = True
+
+    def __init__(
+        self,
+        dataset_name: str = DEFAULT_DATASET,
+        exact_weight: float | None = None,
+        mode: str = DEFAULT_MODE,
+    ) -> None:
+        super().__init__()
+        self.dataset_name = dataset_name
+        self.mode = mode
+        # Exact-match share of the reward; env-var tunable (default 0.4 -> 0.6/0.4).
+        if exact_weight is None:
+            exact_weight = float(os.environ.get("LATEX_OCR_EXACT_WEIGHT", "0.4"))
+        # Length guard against whitespace-padding reward hacks; env-var tunable.
+        overlong_ratio = float(os.environ.get("LATEX_OCR_OVERLONG_RATIO", "4.0"))
+        overlong_floor = int(os.environ.get("LATEX_OCR_OVERLONG_FLOOR", "80"))
+        self._rubric = LatexOCRRubric(
+            exact_weight=exact_weight,
+            overlong_ratio=overlong_ratio,
+            overlong_floor=overlong_floor,
+        )
+        self._state = State(episode_id=str(uuid4()), step_count=0)
+        self._target: Optional[str] = None
+        self._current_split: str = ""
+        self._current_index: int = -1
+        self._current_task_id: str = ""
+        self._done = False
+        # streaming cursor state (per session/instance)
+        self._stream = None
+        self._stream_split: str = ""
+        self._cursor = 0
+        self._exhausted = False
+
+    # ------------------------------------------------------------------ #
+    # Task API                                                            #
+    # ------------------------------------------------------------------ #
+    def list_splits(self) -> list[str]:
+        return _configured_splits()
+
+    def num_tasks(self, split: str) -> int:
+        # Honest denominator in both modes; stream reads metadata only.
+        if self.mode == "stream":
+            total = _split_total(self.dataset_name, split)
+            cap = _max_rows()
+            return min(total, cap) if (cap is not None and total > 0) else total
+        return len(_load_split(self.dataset_name, split))
+
+    def list_tasks(self, split: str) -> list[dict[str, Any]]:
+        n = self.num_tasks(split)
+        if self.mode == "stream":
+            # Positional stubs only; do not enumerate huge splits.
+            preview = min(n if n > 0 else 0, 100)
+            return [
+                {"id": f"{split}-{i}", "index": i, "sequential": True}
+                for i in range(preview)
+            ]
+        return [{"id": f"{split}-{i}", "index": i} for i in range(n)]
+
+    def get_task(self, split: str, index: int) -> dict[str, Any]:
+        n = self.num_tasks(split)
+        if index < 0 or (n > 0 and index >= n):
+            raise IndexError(f"index {index} out of range for split {split} (n={n})")
+        task = {"id": f"{split}-{index}", "index": index, "split": split}
+        if self.mode == "stream":
+            # Position metadata only; stream serves rows sequentially via reset().
+            task["sequential"] = True
+        return task
+
+    def get_task_range(
+        self, split: str, start: int | None = None, stop: int | None = None
+    ) -> list[dict[str, Any]]:
+        n = self.num_tasks(split)
+        start = 0 if start is None else start
+        stop = n if stop is None else (min(stop, n) if n > 0 else stop)
+        # In stream mode `n` comes from metadata and can be enormous; cap the number
+        # of generated stubs so an unbounded range can't OOM the process.
+        if self.mode == "stream" and stop - start > _STREAM_RANGE_CAP:
+            logger.warning(
+                "get_task_range: capping stream range %d..%d to %d stubs",
+                start,
+                stop,
+                _STREAM_RANGE_CAP,
+            )
+            stop = start + _STREAM_RANGE_CAP
+        return [
+            {"id": f"{split}-{i}", "index": i, "split": split}
+            for i in range(start, stop)
+        ]
+
+    # ------------------------------------------------------------------ #
+    # Episode                                                             #
+    # ------------------------------------------------------------------ #
+    def reset(
+        self,
+        split: str = "train",
+        index: int | None = None,
+        seed: int | None = None,
+        episode_id: str | None = None,
+        **kwargs: Any,
+    ) -> LatexOCRObservation:
+        if split not in self.list_splits():
+            raise ValueError(
+                f"unknown split {split!r}; configured splits: {self.list_splits()}"
+            )
+        self._done = False
+        self._state = State(episode_id=episode_id or str(uuid4()), step_count=0)
+        if self.mode == "stream":
+            if index is not None:
+                raise ValueError(
+                    "Random-access reset(index=...) is not supported in stream mode; "
+                    "use materialize mode (LATEX_OCR_MODE=materialize) for indexed access."
+                )
+            return self._reset_stream(split)
+        return self._reset_materialize(split, index, seed)
+
+    def _reset_materialize(
+        self, split: str, index: int | None, seed: int | None
+    ) -> LatexOCRObservation:
+        ds = _load_split(self.dataset_name, split)
+        n = len(ds)
+        if n == 0:
+            raise IndexError(
+                f"split {split!r} is empty (n=0); check the dataset or LATEX_OCR_MAX_ROWS"
+            )
+        if index is None:
+            index = random.Random(seed).randrange(n)
+        if index < 0 or index >= n:
+            raise IndexError(f"index {index} out of range for split {split} (n={n})")
+        row = ds[int(index)]
+        self._target = str(row[TEXT_COLUMN])
+        self._current_split, self._current_index = split, int(index)
+        self._current_task_id = f"{split}-{index}"
+        return LatexOCRObservation(
+            done=False,
+            image_base64=_encode_image(row[IMAGE_COLUMN]),
+            image_format="png",
+            prompt=DEFAULT_PROMPT,
+            split=split,
+            index=int(index),
+            task_id=f"{split}-{index}",
+        )
+
+    def _ensure_stream(self, split: str) -> None:
+        if (
+            self._stream is not None
+            and self._stream_split == split
+            and not self._exhausted
+        ):
+            return
+        from datasets import load_dataset
+
+        ds = load_dataset(
+            self.dataset_name,
+            name=CONFIG,
+            split=split,
+            streaming=True,
+            token=os.environ.get("HF_TOKEN"),
+        )
+        if SHUFFLE_BUFFER > 0:
+            ds = ds.shuffle(seed=SEED, buffer_size=SHUFFLE_BUFFER)
+        self._stream = iter(ds)
+        self._stream_split = split
+        self._cursor = 0
+        self._exhausted = False
+
+    def _stream_exhausted(self, split: str, total: int) -> LatexOCRObservation:
+        # Mark the episode finished and clear the target so a following step() fails
+        # fast instead of grading against the previous (stale) task.
+        self._exhausted = True
+        self._done = True
+        self._target = None
+        return LatexOCRObservation(
+            done=True,
+            split=split,
+            index=self._cursor,
+            total=total,
+            remaining=0,
+            pct_done=1.0,
+            exhausted=True,
+            task_id=f"{split}-stream-end",
+        )
+
+    def _reset_stream(self, split: str) -> LatexOCRObservation:
+        self._ensure_stream(split)
+        total = self.num_tasks(split)  # honors LATEX_OCR_MAX_ROWS
+        cap = _max_rows()
+        if cap is not None and self._cursor >= cap:
+            return self._stream_exhausted(split, total)
+        try:
+            row = next(self._stream)
+        except StopIteration:
+            return self._stream_exhausted(split, total)
+        self._cursor += 1
+        self._target = str(row[TEXT_COLUMN])
+        self._current_split, self._current_index = split, self._cursor
+        self._current_task_id = f"{split}-stream-{self._cursor}"
+        remaining = (total - self._cursor) if total > 0 else -1
+        return LatexOCRObservation(
+            done=False,
+            image_base64=_encode_image(row[IMAGE_COLUMN]),
+            image_format="png",
+            prompt=DEFAULT_PROMPT,
+            split=split,
+            index=self._cursor,
+            task_id=f"{split}-stream-{self._cursor}",
+            total=total,
+            remaining=remaining,
+            pct_done=round(self._cursor / total, 6) if total > 0 else 0.0,
+            exhausted=False,
+        )
+
+    def step(self, action: LatexOCRAction, **kwargs: Any) -> LatexOCRObservation:
+        if self._target is None:
+            raise RuntimeError("step() called before reset()")
+        if self._done:
+            raise RuntimeError("Episode already terminated; call reset() first")
+        prediction = action.latex if isinstance(action, LatexOCRAction) else ""
+        result = self._rubric.grade(prediction, self._target)
+        self._state.step_count += 1
+        self._done = True
+
+        total = (
+            _split_total(self.dataset_name, self._current_split)
+            if self.mode == "stream"
+            else -1
+        )
+        return LatexOCRObservation(
+            done=True,
+            reward=result.reward,
+            split=self._current_split,
+            index=self._current_index,
+            task_id=self._current_task_id,
+            predicted_latex=prediction,
+            target_latex=self._target,
+            exact_match=result.exact_match,
+            char_error_rate=result.char_error_rate,
+            total=total,
+            remaining=(total - self._cursor)
+            if (self.mode == "stream" and total > 0)
+            else -1,
+            pct_done=round(self._cursor / total, 6)
+            if (self.mode == "stream" and total > 0)
+            else 0.0,
+            metadata={
+                "exact_match": result.exact_match,
+                "char_error_rate": result.char_error_rate,
+                "mode": self.mode,
+            },
+        )
+
+    @property
+    def state(self) -> State:
+        return self._state
diff --git a/envs/latex_ocr_env/server/rubric.py b/envs/latex_ocr_env/server/rubric.py
new file mode 100644
index 000000000..06f4832ec
--- /dev/null
+++ b/envs/latex_ocr_env/server/rubric.py
@@ -0,0 +1,170 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Reward computation for the LaTeX OCR environment.
+
+A smooth, dense reward: normalized character edit distance with a small
+exact-match bonus, which is the standard shape for OCR-style RL where an
+exact-string reward alone is too sparse (and discontinuous auxiliary terms make
+training spiky):
+
+    reward = [(1 - exact_weight) * (1 - CER) + exact_weight * exact_match] * length_factor
+
+where ``CER`` is the normalized character error rate (Levenshtein distance over
+the longer whitespace-stripped string). A perfect transcription scores 1.0; a
+partial one is capped at ``1 - exact_weight``. Whitespace-insensitive, since
+LaTeX spacing is cosmetic.
+
+**Length guard.** Because the score is whitespace-insensitive, a policy could
+otherwise emit the correct answer followed by unlimited whitespace and still
+score 1.0 — a reward hack that shows up in RL as completions drifting to the
+generation-length cap ("correct LaTeX + padding"). ``length_factor`` closes it:
+the reward decays once the *raw* prediction grows past a multiple of the target
+length (padding is measured before it is stripped away). Normal LaTeX spacing
+stays well within the allowance, so legitimate answers are unaffected.
+
+The rubric also cleans the raw completion itself (strips markdown code fences
+and surrounding math delimiters) so it can score the model's raw output
+directly and measure its true length. Cleaning is idempotent, so callers that
+pre-clean their text still work unchanged.
+
+Implemented with a pure-Python Levenshtein so the environment carries no extra
+runtime dependency.
+"""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+
+
+_WHITESPACE = re.compile(r"\s+")
+_FENCE = re.compile(r"```[a-zA-Z]*\s*(.*?)\s*```", re.DOTALL)
+
+# Length-guard defaults. A correct transcription is about as long as the target;
+# allow up to ``ratio`` times the target length (with a small floor so very short
+# targets get breathing room) before the reward starts to decay.
+_DEFAULT_OVERLONG_RATIO = 4.0
+_DEFAULT_OVERLONG_FLOOR = 80
+
+
+def normalize_latex(text: str) -> str:
+    """Collapse whitespace runs to single spaces and strip."""
+    return _WHITESPACE.sub(" ", text or "").strip()
+
+
+def _strip_all_whitespace(text: str) -> str:
+    return _WHITESPACE.sub("", text or "")
+
+
+def clean_latex(text: str) -> str:
+    """Strip markdown code fences and surrounding math delimiters from a completion.
+
+    Idempotent: already-clean LaTeX is returned unchanged. Applied inside the rubric so it
+    can score the model's raw output directly (and measure its true length for the guard).
+    """
+    text = (text or "").strip()
+    m = _FENCE.search(text)
+    if m:
+        text = m.group(1)
+    return text.strip().strip("$").strip()
+
+
+def levenshtein(a: str, b: str) -> int:
+    """Edit distance between two strings (pure Python, O(len(a) * len(b)))."""
+    if a == b:
+        return 0
+    if not a:
+        return len(b)
+    if not b:
+        return len(a)
+    previous = list(range(len(b) + 1))
+    for i, ca in enumerate(a, start=1):
+        current = [i]
+        for j, cb in enumerate(b, start=1):
+            insert = current[j - 1] + 1
+            delete = previous[j] + 1
+            substitute = previous[j - 1] + (ca != cb)
+            current.append(min(insert, delete, substitute))
+        previous = current
+    return previous[-1]
+
+
+@dataclass
+class GradeResult:
+    reward: float
+    exact_match: bool
+    char_error_rate: float
+    length_factor: float = (
+        1.0  # < 1.0 when the raw prediction was penalized for being overlong
+    )
+
+
+class LatexOCRRubric:
+    """Normalized edit distance + exact-match bonus, with a length guard against padding.
+
+    Args:
+        exact_weight: fraction of the reward reserved for an exact match. With
+            the default 0.4, partial answers score in ``[0, 0.6]`` and only an
+            exact (whitespace-insensitive) match reaches ``1.0``.
+        overlong_ratio: the raw prediction may be up to ``overlong_ratio`` times
+            the target length before the reward decays; it reaches 0 at twice the
+            allowance. Set ``<= 0`` to disable the length guard.
+        overlong_floor: minimum allowed raw length (chars), so very short targets
+            still tolerate normal formatting before the guard engages.
+    """
+
+    def __init__(
+        self,
+        exact_weight: float = 0.4,
+        overlong_ratio: float = _DEFAULT_OVERLONG_RATIO,
+        overlong_floor: int = _DEFAULT_OVERLONG_FLOOR,
+    ) -> None:
+        if not 0.0 <= exact_weight <= 1.0:
+            raise ValueError("exact_weight must be in [0, 1]")
+        self.exact_weight = exact_weight
+        self.overlong_ratio = overlong_ratio
+        self.overlong_floor = overlong_floor
+
+    def grade(self, prediction: str, target: str) -> GradeResult:
+        raw = prediction or ""
+        # Clean the raw completion (fences, $-delimiters) before scoring; LaTeX spacing is
+        # cosmetic, so score on the whitespace-stripped form: an exact match => CER 0.
+        cleaned = clean_latex(raw)
+        pred_canon = _strip_all_whitespace(normalize_latex(cleaned))
+        target_norm = normalize_latex(target)
+        target_canon = _strip_all_whitespace(target_norm)
+
+        exact = pred_canon == target_canon
+        if not target_canon:
+            cer = 0.0 if not pred_canon else 1.0
+        else:
+            distance = levenshtein(pred_canon, target_canon)
+            cer = min(1.0, distance / max(len(pred_canon), len(target_canon)))
+
+        similarity = 1.0 - cer
+        reward = (1.0 - self.exact_weight) * similarity + self.exact_weight * float(
+            exact
+        )
+
+        # Length guard: penalize predictions whose RAW length (measured before whitespace is
+        # stripped, so padding counts) far exceeds the target's. Reward decays linearly with
+        # the excess and reaches 0 at twice the allowance.
+        length_factor = 1.0
+        if self.overlong_ratio > 0:
+            allowed = max(self.overlong_floor, self.overlong_ratio * len(target_norm))
+            if len(raw) > allowed:
+                over = (len(raw) - allowed) / allowed
+                length_factor = max(0.0, 1.0 - over)
+                reward *= length_factor
+
+        return GradeResult(
+            reward=round(reward, 6),
+            exact_match=exact,
+            char_error_rate=round(cer, 6),
+            length_factor=round(length_factor, 6),
+        )
diff --git a/envs/latex_ocr_env/uv.lock b/envs/latex_ocr_env/uv.lock
new file mode 100644
index 000000000..64ebc5d2e
--- /dev/null
+++ b/envs/latex_ocr_env/uv.lock
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diff --git a/envs/latex_ocr_env/validate.py b/envs/latex_ocr_env/validate.py
new file mode 100644
index 000000000..6decb788f
--- /dev/null
+++ b/envs/latex_ocr_env/validate.py
@@ -0,0 +1,143 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+End-to-end driver for the LaTeX OCR environment.
+
+Connects to a running server, exercises the Task API, resets to a task, runs a
+policy over the image, steps, and prints the reward. The policy is either a
+real vision-LLM served through the Hugging Face Inference Router
+(OpenAI-compatible) or, if no HF_TOKEN is set, a no-op placeholder so the
+plumbing can still be verified.
+
+Usage:
+    # Start the server first (see README), then:
+    python validate.py --base-url http://localhost:8000 \\
+        --split test --num 3 --model Qwen/Qwen2.5-VL-7B-Instruct
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+
+# Make `latex_ocr_env` importable when run from the env directory.
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from latex_ocr_env import LatexOCRAction, LatexOCREnv  # noqa: E402
+
+
+def vlm_transcribe(image_base64: str, prompt: str, model: str) -> str:
+    """Run a VLM over the image via the Hugging Face Inference Router."""
+    from openai import OpenAI
+
+    client = OpenAI(
+        base_url="https://router.huggingface.co/v1",
+        api_key=os.environ["HF_TOKEN"],
+    )
+    completion = client.chat.completions.create(
+        model=model,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/png;base64,{image_base64}"},
+                    },
+                ],
+            }
+        ],
+    )
+    text = completion.choices[0].message.content or ""
+    # Strip common code-fence wrapping so scoring sees raw LaTeX.
+    text = text.strip()
+    for fence in ("```latex", "```LaTeX", "```"):
+        if text.startswith(fence):
+            text = text[len(fence) :]
+        if text.endswith("```"):
+            text = text[:-3]
+    return text.strip()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base-url", default="http://localhost:8000")
+    parser.add_argument("--split", default="test")
+    parser.add_argument("--num", type=int, default=3, help="Tasks to run")
+    parser.add_argument(
+        "--model",
+        default=os.environ.get("LATEX_OCR_MODEL", "Qwen/Qwen2.5-VL-7B-Instruct"),
+        help="VLM served via the HF router (append :provider to pin, e.g. ':nebius').",
+    )
+    args = parser.parse_args()
+
+    use_vlm = bool(os.environ.get("HF_TOKEN"))
+    if not use_vlm:
+        print("HF_TOKEN not set -> running plumbing-only policy (no VLM).\n")
+
+    with LatexOCREnv(base_url=args.base_url).sync() as env:
+        # --- Task API ---
+        splits = env.list_splits()
+        n = env.num_tasks(args.split)
+        print(f"splits = {splits}")
+        print(f"num_tasks({args.split}) = {n}")
+        print(f"get_task({args.split}, 0) = {env.get_task(args.split, 0)}\n")
+
+        rewards = []
+        seen_targets = []
+        # n <= 0 means unknown count (stream metadata); fall back to --num.
+        count = args.num if n <= 0 else min(args.num, n)
+        stream_mode = False
+        for i in range(count):
+            if stream_mode:
+                result = env.reset(split=args.split)
+            else:
+                try:
+                    result = env.reset(split=args.split, index=i)
+                except Exception:
+                    # stream-mode server rejects random index -> pull sequentially
+                    stream_mode = True
+                    result = env.reset(split=args.split)
+            obs = result.observation
+            prog = ""
+            if obs.total and obs.total > 0:
+                prog = (
+                    f" | progress: {obs.index}/{obs.total} "
+                    f"({obs.pct_done:.4%}), remaining={obs.remaining}"
+                )
+            print(
+                f"[task {obs.task_id}] image bytes(b64)={len(obs.image_base64)}{prog}"
+            )
+
+            if use_vlm:
+                prediction = vlm_transcribe(obs.image_base64, obs.prompt, args.model)
+            else:
+                prediction = ""  # plumbing check only
+
+            result = env.step(LatexOCRAction(latex=prediction))
+            o = result.observation
+            rewards.append(result.reward)
+            seen_targets.append(o.target_latex)
+            print(f"  predicted : {o.predicted_latex[:80]!r}")
+            print(f"  target    : {o.target_latex[:80]!r}")
+            print(
+                f"  reward={result.reward:.4f} exact={o.exact_match} "
+                f"cer={o.char_error_rate:.4f}\n"
+            )
+
+        if rewards:
+            print(
+                f"\nmean reward over {len(rewards)} tasks = {sum(rewards) / len(rewards):.4f}"
+            )
+            uniq = len(set(seen_targets))
+            print(f"no-repeat check: {uniq}/{len(seen_targets)} distinct targets")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/grpo_latex_ocr/README.md b/examples/grpo_latex_ocr/README.md
new file mode 100644
index 000000000..901f70645
--- /dev/null
+++ b/examples/grpo_latex_ocr/README.md
@@ -0,0 +1,59 @@
+# Training a Vision Model to Read Math with GRPO + OpenEnv
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/OpenEnv/blob/add-latex-ocr-multimodal-streaming-env/examples/grpo_latex_ocr/grpo_latex_ocr_tutorial.ipynb)
+
+A beginner-friendly tutorial that uses **GRPO (Group Relative Policy Optimization)** to teach a
+**vision-language model** to transcribe images of math formulas into **LaTeX** — with the images
+*and* the reward supplied by the [`latex_ocr_env`](../../envs/latex_ocr_env) OpenEnv environment.
+
+## 🎯 What This Example Shows
+
+- **OpenEnv as a reward source**: the environment serves each formula image (`reset`) and grades the
+  model's LaTeX server-side (`step`) — no reward code to write yourself.
+- **GRPO with TRL**: `GRPOTrainer` + `environment_factory`, LoRA adapters, on a single GPU.
+- **Eval before & after**: score the model on the held-out `test` split (same reward used in
+  training) to measure the actual improvement (the delta).
+- **Live tracking**: reward/loss curves stream to a [Trackio](https://huggingface.co/blog/trackio)
+  dashboard, embedded right in the notebook.
+- **Ship it**: merge the LoRA adapter and push the trained model to the Hub.
+
+## 📁 Files
+
+- `grpo_latex_ocr_tutorial.ipynb` — the tutorial notebook (start here)
+- `README.md` — this file
+
+## 🚀 Quick Start
+
+Open the notebook in Colab (badge above) and run top to bottom. A GPU is required — an **A100** is
+recommended for the 4B model; drop to a 2B model or fewer generations on smaller GPUs.
+
+The notebook is self-contained: it `pip install`s TRL and the environment package, so no local
+checkout is needed. By default it connects to the **hosted** environment Space
+([`AdithyaSK/latex-ocr-env`](https://huggingface.co/spaces/AdithyaSK/latex-ocr-env)); set
+`USE_LOCAL_ENV = True` to run the environment locally instead.
+
+## 🧩 The Environment
+
+See [`envs/latex_ocr_env`](../../envs/latex_ocr_env) for the full environment: a dataset-backed,
+single-step (bandit) RL task with a weighted, server-side reward (edit similarity, exact match,
+structural validity, length/format). It ships `train` and `test` splits.
+
+## 📈 Results
+
+We ran this GRPO setup against the environment on four open vision-language models (colocated vLLM,
+8 generations per prompt, held-out `test` reward scored every 500 steps — the same reward used in
+training). All four **improve monotonically and stay stable**:
+
+![Training curves](assets/training_curves.png)
+
+| Model | Base eval | Best eval | Δ (absolute) | Δ (relative) |
+|---|---|---|---|---|
+| **GLM-OCR** | 0.449 | 0.665 | **+0.215** | **+48%** |
+| **Gemma-4-E2B** | 0.392 | 0.570 | +0.178 | +45% |
+| **Qwen3-VL-2B** | 0.615 | **0.723** | +0.108 | +18% |
+| **Qwen3.5-2B** | 0.688 | 0.711 | +0.022 | +3% |
+
+*Reward is the environment's own score (0–1). The models with the most headroom (GLM-OCR, Gemma) see
+the largest gains; Qwen3.5-2B already starts near the ceiling. Qwen3-VL-2B reaches the highest final
+score with the cleanest curve. Numbers are for the hosted [`AdithyaSK/latex-ocr-env`](https://huggingface.co/spaces/AdithyaSK/latex-ocr-env)
+environment; your exact deltas will vary with model, generations, and step budget.*
diff --git a/examples/grpo_latex_ocr/assets/training_curves.png b/examples/grpo_latex_ocr/assets/training_curves.png
new file mode 100644
index 000000000..2c57a1439
Binary files /dev/null and b/examples/grpo_latex_ocr/assets/training_curves.png differ
diff --git a/examples/grpo_latex_ocr/grpo_latex_ocr_tutorial.ipynb b/examples/grpo_latex_ocr/grpo_latex_ocr_tutorial.ipynb
new file mode 100644
index 000000000..d82fcb6f0
--- /dev/null
+++ b/examples/grpo_latex_ocr/grpo_latex_ocr_tutorial.ipynb
@@ -0,0 +1,508 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# \ud83e\uddee Teach a Vision Model to Read Math \u2014 GRPO + OpenEnv\n",
+    "\n",
+    "Reinforcement-learning a **vision-language model** to turn images of math formulas into **LaTeX**, using TRL's\n",
+    "`GRPOTrainer` and an **OpenEnv** environment that supplies *both* the image and the reward.\n",
+    "\n",
+    "**What you'll do:**\n",
+    "1. Install + connect to the LaTeX-OCR environment (hosted, or run it locally)\n",
+    "2. **Evaluate the base model** on held-out test images (a starting score)\n",
+    "3. **Train** with GRPO (live reward curve on a Trackio dashboard)\n",
+    "4. **Evaluate again** and see the improvement (the delta)\n",
+    "5. **Push** your model to the Hub\n",
+    "\n",
+    "> \ud83d\udca1 A GPU is required. An **A100** (Colab Pro / a beefy runtime) is recommended for a 4B model; drop to a 2B model or\n",
+    "> fewer generations on smaller GPUs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Install\n",
+    "\n",
+    "\u23f3 Takes ~2\u20133 min and prints nothing (output is hidden). The `[*]` turns into a number when it's done \u2014 then move on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "# trl (from source: VLM + environment_factory) + the LaTeX-OCR OpenEnv package + LoRA/util deps\n",
+    "!pip install -q \"trl @ git+https://github.com/huggingface/trl.git\" \\\n",
+    "    \"openenv-latex_ocr_env @ git+https://github.com/adithya-s-k/OpenEnv.git@add-latex-ocr-multimodal-streaming-env#subdirectory=envs/latex_ocr_env\" \\\n",
+    "    peft trackio ipywidgets fastmcp websockets jmespath hf_transfer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## \ud83d\udd11 Log in to Hugging Face\n",
+    "\n",
+    "You need a free HF account and a **token with _write_ access** \u2014 create one at\n",
+    "[huggingface.co/settings/tokens](https://huggingface.co/settings/tokens). The token lets the notebook create\n",
+    "your Trackio dashboard Space and push the trained model to the Hub. Run the cell and paste it in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import notebook_login\n",
+    "notebook_login()   # paste a WRITE token   (or instead: import os; os.environ[\"HF_TOKEN\"] = \"hf_...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Settings\n",
+    "\n",
+    "OpenEnv is a tiny server that **hands you a formula image** and **scores your LaTeX** (edit-distance + exact-match,\n",
+    "computed server-side). By default we use the free **hosted Space** \u2014 zero setup. Flip `USE_LOCAL_ENV = True` to run\n",
+    "the same environment on this machine instead (next cell)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from huggingface_hub import whoami\n",
+    "os.environ[\"HF_HUB_ENABLE_HF_TRANSFER\"] = \"1\"\n",
+    "\n",
+    "USERNAME           = whoami()[\"name\"]        # from your login above \u2014 used to name your repos\n",
+    "\n",
+    "MODEL              = \"Qwen/Qwen3.5-4B\"      # any image-text-to-text VLM (e.g. Qwen/Qwen3.5-2B for smaller GPUs)\n",
+    "USE_LOCAL_ENV      = False                   # False = hosted Space (no setup); True = serve locally (next cell)\n",
+    "ENV_URL            = \"https://AdithyaSK-latex-ocr-env.hf.space\"\n",
+    "\n",
+    "N_EVAL_SAMPLES     = 50                       # test images to score for eval\n",
+    "NUM_TRAIN_SAMPLES  = 500                      # env tasks to train on\n",
+    "MAX_STEPS          = 30                        # optimization steps (controls how many tasks are actually consumed)\n",
+    "NUM_GENERATIONS    = 8\n",
+    "\n",
+    "# Your repos (auto-named from your username). Set PUSH_REPO_ID = \"\" to skip pushing the model.\n",
+    "PUSH_REPO_ID       = f\"{USERNAME}/qwen3.5-latex-ocr-grpo\"   # trained model goes here\n",
+    "\n",
+    "# Experiment tracking (Trackio). Turn OFF to train without any dashboard / Space.\n",
+    "USE_TRACKIO        = True\n",
+    "TRACKIO_SPACE_ID   = f\"{USERNAME}/trackio-latex-ocr\"        # dashboard Space (auto-created when USE_TRACKIO)\n",
+    "TRACKIO_PROJECT    = \"latex-ocr-grpo\"\n",
+    "\n",
+    "print(f\"\ud83d\udc64 logged in as   : {USERNAME}\")\n",
+    "print(f\"\ud83e\udde0 model          : {MODEL}\")\n",
+    "print(f\"\ud83c\udf10 environment    : {'local (next cell)' if USE_LOCAL_ENV else ENV_URL}\")\n",
+    "print(f\"\ud83c\udfcb\ufe0f  train / steps  : {NUM_TRAIN_SAMPLES} tasks available \u00b7 {MAX_STEPS} steps \u00b7 {NUM_GENERATIONS} generations\")\n",
+    "print(f\"\ud83d\udccf eval samples   : {N_EVAL_SAMPLES}\")\n",
+    "print(f\"\ud83d\udcc8 tracking       : {'Trackio \u2192 ' + TRACKIO_SPACE_ID if USE_TRACKIO else 'off'}\")\n",
+    "print(f\"\ud83d\udce4 push to        : {PUSH_REPO_ID or '(skipped)'}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (Optional) Run the environment locally\n",
+    "\n",
+    "Only needed if `USE_LOCAL_ENV = True`. Starts the OpenEnv LaTeX-OCR server on this machine (CPU-only, runs\n",
+    "alongside your GPU training) and points `ENV_URL` at it.\n",
+    "\n",
+    "Two knobs keep it fast:\n",
+    "- **`LATEX_OCR_MODE=materialize`** \u2014 loads + indexes the split so `reset(split, index)` is O(1) random access.\n",
+    "  (The other mode, `stream`, is a sequential cursor with *no* random access \u2014 our training/eval need indices, so\n",
+    "  streaming would be unusably slow here.)\n",
+    "- **`LATEX_OCR_MAX_ROWS`** \u2014 materialize would otherwise decode the *entire* split (68k+ train images). We cap it to\n",
+    "  just the rows we actually touch, so the server is ready in seconds instead of minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "if USE_LOCAL_ENV:\n",
+    "    import subprocess, sys, time, requests\n",
+    "    subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"gradio\"], check=True)  # env server's UI import\n",
+    "    os.environ[\"LATEX_OCR_MODE\"]        = \"materialize\"                    # random-access reset(split, index)\n",
+    "    os.environ[\"LATEX_OCR_MAX_ROWS\"]    = str(max(NUM_TRAIN_SAMPLES, N_EVAL_SAMPLES))  # only load what we use\n",
+    "    os.environ[\"LATEX_OCR_MAX_SESSIONS\"] = str(max(16, NUM_GENERATIONS))   # allow concurrent rollouts\n",
+    "    _server = subprocess.Popen([\"python\", \"-c\", \"from latex_ocr_env.server.app import main; main()\"])\n",
+    "    ENV_URL = \"http://localhost:8000\"\n",
+    "    for _ in range(90):\n",
+    "        try:\n",
+    "            if requests.get(f\"{ENV_URL}/healthz\", timeout=2).ok:\n",
+    "                print(\"\u2705 local env server ready:\", ENV_URL); break\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "        time.sleep(2)\n",
+    "    else:\n",
+    "        raise RuntimeError(\"local env server did not start \u2014 check the logs above\")\n",
+    "else:\n",
+    "    print(\"Using hosted environment:\", ENV_URL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. The environment wrapper + reward\n",
+    "\n",
+    "`GRPOTrainer` talks to an environment through a small class: `reset()` returns the **image + instruction** (TRL puts\n",
+    "the image straight into the VLM's prompt), and the reward function submits the model's LaTeX with `step()` and reads\n",
+    "the **server-computed reward**.\n",
+    "\n",
+    "**The dataset lives inside the environment.** It serves `train` and `test` splits of formula images; we never load a\n",
+    "dataset ourselves. Our local \"dataset\" is just a list of `{split, index}` rows telling the trainer which task to\n",
+    "`reset()` to \u2014 so every generation in a group sees the same image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import base64, re\n",
+    "from io import BytesIO\n",
+    "from PIL import Image\n",
+    "from datasets import Dataset\n",
+    "from latex_ocr_env import LatexOCRAction, LatexOCREnv\n",
+    "\n",
+    "INSTRUCTION = \"Transcribe the mathematical formula in the image to LaTeX. Output only the LaTeX code.\"\n",
+    "\n",
+    "def _strip_fences(t):\n",
+    "    t = (t or \"\").strip()\n",
+    "    m = re.search(r\"```(?:latex)?\\s*(.*?)\\s*```\", t, flags=re.DOTALL)\n",
+    "    if m: t = m.group(1)\n",
+    "    return t.strip().strip(\"$\").strip()\n",
+    "\n",
+    "def _decode(b64, max_px=512):\n",
+    "    img = Image.open(BytesIO(base64.b64decode(b64))).convert(\"RGB\")\n",
+    "    img.thumbnail((max_px, max_px), Image.LANCZOS)\n",
+    "    return img\n",
+    "\n",
+    "class LatexOCREnv_GRPO:\n",
+    "    \"\"\"environment_factory: the env supplies the image (reset) and the reward (step).\"\"\"\n",
+    "    def __init__(self):\n",
+    "        self.client = LatexOCREnv(base_url=ENV_URL, connect_timeout_s=60, message_timeout_s=180)\n",
+    "    def reset(self, split=\"train\", index=None, **kw):\n",
+    "        idx = int(index) if index is not None else None\n",
+    "        obs = self.client.reset(split=split, index=idx).observation\n",
+    "        return [{\"type\": \"image\", \"image\": _decode(obs.image_base64)},\n",
+    "                {\"type\": \"text\", \"text\": obs.prompt or INSTRUCTION}]\n",
+    "\n",
+    "def env_reward(completions, environments, **kw):\n",
+    "    return [float(e.client.step(LatexOCRAction(latex=_strip_fences(c[0][\"content\"]))).reward or 0.0)\n",
+    "            for e, c in zip(environments, completions)]\n",
+    "\n",
+    "def build_dataset(n, split=\"train\"):\n",
+    "    probe = LatexOCREnv(base_url=ENV_URL, connect_timeout_s=60)\n",
+    "    k = min(n, probe.num_tasks(split))\n",
+    "    print(f\"{split}: using {k} of {probe.num_tasks(split)} tasks\")\n",
+    "    return Dataset.from_dict({\"split\": [split] * k, \"index\": list(range(k))})\n",
+    "\n",
+    "# --- peek: connect to the env and show one task so you can see what the model sees ---\n",
+    "from IPython.display import display\n",
+    "_probe = LatexOCREnv(base_url=ENV_URL, connect_timeout_s=60)\n",
+    "print(f\"\u2705 connected to env  \u00b7  train tasks: {_probe.num_tasks('train')}  \u00b7  test tasks: {_probe.num_tasks('test')}\")\n",
+    "_obs = _probe.reset(split=\"train\", index=0).observation\n",
+    "print(f\"prompt: {(_obs.prompt or INSTRUCTION)}\")\n",
+    "print(\"example image the model must transcribe:\")\n",
+    "display(_decode(_obs.image_base64))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Evaluation\n",
+    "\n",
+    "A simple, honest score: for `N` **test-split** images, let the model generate LaTeX and grade it with the **same\n",
+    "reward** used in training. The average is our metric. We'll run it before *and* after training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from tqdm.auto import tqdm\n",
+    "from IPython.display import display\n",
+    "\n",
+    "def generate_latex(model, processor, image, prompt, max_new_tokens=256):\n",
+    "    msgs = [{\"role\": \"user\", \"content\": [{\"type\": \"image\", \"image\": image}, {\"type\": \"text\", \"text\": prompt}]}]\n",
+    "    inputs = processor.apply_chat_template(\n",
+    "        msgs, add_generation_prompt=True, tokenize=True, return_dict=True,\n",
+    "        return_tensors=\"pt\", enable_thinking=False,\n",
+    "    ).to(model.device)\n",
+    "    with torch.no_grad():\n",
+    "        out = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)\n",
+    "    return processor.batch_decode(out[:, inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True)[0]\n",
+    "\n",
+    "def evaluate(model, processor, n_samples, split=\"test\", show=3):\n",
+    "    \"\"\"Average env reward over the first N `split` images (greedy). Prints progress + a few examples.\"\"\"\n",
+    "    client = LatexOCREnv(base_url=ENV_URL, connect_timeout_s=60, message_timeout_s=180)\n",
+    "    # Generation is ~10x faster with the KV cache. After training, the model still has gradient\n",
+    "    # checkpointing on (which forces use_cache=False) and is in train mode \u2014 so switch to a fast\n",
+    "    # eval state here and restore it afterwards, so before/after evals run at the same speed.\n",
+    "    was_training = model.training\n",
+    "    was_gc = getattr(model, \"is_gradient_checkpointing\", False)\n",
+    "    model.eval()\n",
+    "    if was_gc: model.gradient_checkpointing_disable()\n",
+    "    model.config.use_cache = True\n",
+    "    scores, examples = [], []\n",
+    "    try:\n",
+    "        pbar = tqdm(range(n_samples), desc=f\"evaluating [{split}]\")\n",
+    "        for i in pbar:\n",
+    "            obs = client.reset(split=split, index=i).observation\n",
+    "            img = _decode(obs.image_base64)\n",
+    "            pred = generate_latex(model, processor, img, obs.prompt or INSTRUCTION)\n",
+    "            r = float(client.step(LatexOCRAction(latex=_strip_fences(pred))).reward or 0.0)\n",
+    "            scores.append(r)\n",
+    "            pbar.set_postfix(avg_reward=f\"{sum(scores) / len(scores):.3f}\")   # live running average\n",
+    "            if i < show: examples.append((img, _strip_fences(pred), r))\n",
+    "    finally:\n",
+    "        model.config.use_cache = False\n",
+    "        if was_gc: model.gradient_checkpointing_enable()\n",
+    "        if was_training: model.train()\n",
+    "    mean = sum(scores) / len(scores)\n",
+    "    print(f\"\\n\ud83d\udcca mean reward over {n_samples} [{split}] samples = {mean:.4f}\\n\")\n",
+    "    for j, (img, pred, r) in enumerate(examples):\n",
+    "        print(f\"\u2014 example {j + 1}  \u00b7  reward {r:.3f}\")\n",
+    "        display(img)\n",
+    "        print(f\"   model output: {pred[:160]}\\n\")\n",
+    "    return mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Build the trainer\n",
+    "\n",
+    "We load the VLM with **LoRA** (adapters \u2014 light on memory) and hand `GRPOTrainer` the `environment_factory` +\n",
+    "`env_reward`. `enable_thinking=False` keeps Qwen from emitting a long `<think>` block so it outputs LaTeX directly.\n",
+    "\n",
+    "We also turn on **[Trackio](https://huggingface.co/blog/trackio)** \u2014 a free, local-first experiment tracker with a\n",
+    "`wandb`-style API. Setting `report_to=\"trackio\"` + `trackio_space_id` streams the reward/loss/KL curves live to a\n",
+    "Hugging Face Space (a real-time dashboard you can share). We'll embed it right in this notebook after training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from trl import GRPOConfig, GRPOTrainer\n",
+    "from peft import LoraConfig\n",
+    "\n",
+    "train_dataset = build_dataset(NUM_TRAIN_SAMPLES, \"train\")\n",
+    "\n",
+    "peft_config = LoraConfig(task_type=\"CAUSAL_LM\", r=16, lora_alpha=32, lora_dropout=0.05,\n",
+    "                         target_modules=[\"q_proj\", \"v_proj\"])\n",
+    "\n",
+    "trainer = GRPOTrainer(\n",
+    "    model=MODEL,\n",
+    "    train_dataset=train_dataset,\n",
+    "    reward_funcs=env_reward,\n",
+    "    environment_factory=LatexOCREnv_GRPO,\n",
+    "    peft_config=peft_config,\n",
+    "    args=GRPOConfig(\n",
+    "        output_dir=\"latex_ocr_grpo\",\n",
+    "        model_init_kwargs={\"dtype\": \"bfloat16\"},\n",
+    "        num_generations=NUM_GENERATIONS,\n",
+    "        per_device_train_batch_size=NUM_GENERATIONS,\n",
+    "        max_steps=MAX_STEPS,\n",
+    "        max_completion_length=256,\n",
+    "        temperature=0.9,\n",
+    "        learning_rate=1e-5,\n",
+    "        chat_template_kwargs={\"enable_thinking\": False},\n",
+    "        mask_truncated_completions=True,\n",
+    "        bf16=True,\n",
+    "        gradient_checkpointing=True,\n",
+    "        logging_steps=1, save_strategy=\"no\",\n",
+    "        # --- Trackio logging (toggled by USE_TRACKIO) ---\n",
+    "        report_to=(\"trackio\" if USE_TRACKIO else \"none\"),\n",
+    "        run_name=\"train\",\n",
+    "        project=TRACKIO_PROJECT,\n",
+    "        trackio_space_id=(TRACKIO_SPACE_ID if USE_TRACKIO else None),\n",
+    "        trackio_static_space_id=False,   # keep the live Space (skip freezing it)\n",
+    "    ),\n",
+    ")\n",
+    "processor = trainer.processing_class\n",
+    "\n",
+    "_trainable = trainer.model.num_parameters(only_trainable=True) / 1e6\n",
+    "print(f\"\u2705 trainer ready  \u00b7  {MODEL}\")\n",
+    "print(f\"   trainable params: {_trainable:.1f}M (LoRA)  \u00b7  dataset: {len(train_dataset)} env tasks  \u00b7  {MAX_STEPS} steps\")\n",
+    "print(f\"   metrics \u2192 Trackio project '{TRACKIO_PROJECT}' on {TRACKIO_SPACE_ID}\" if USE_TRACKIO else \"   tracking: off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Baseline \u2014 how good is it *before* training?\n",
+    "\n",
+    "(With LoRA freshly initialized, this is effectively the base model's score.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "print(\"Scoring the untrained model on the test split \u2014 watch the examples below...\")\n",
+    "baseline = evaluate(trainer.model, processor, N_EVAL_SAMPLES, split=\"test\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Train\n",
+    "\n",
+    "Each step prints its metrics (loss, KL, and `rewards/env_reward/mean` \u2014 the number that should climb). The same\n",
+    "curves stream live to your Trackio dashboard, which we embed a couple of cells down."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "print(\"\ud83d\ude80 training \u2014 watch 'rewards/env_reward/mean' trend up over the steps below\\n\")\n",
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Did it improve?\n",
+    "\n",
+    "Same `N` test images, same reward \u2014 now with the trained adapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "trained = evaluate(trainer.model, processor, N_EVAL_SAMPLES, split=\"test\")\n",
+    "print(\"=\" * 48)\n",
+    "print(f\"  before training : {baseline:.4f}\")\n",
+    "print(f\"  after training  : {trained:.4f}\")\n",
+    "print(f\"  improvement (\u0394) : {trained - baseline:+.4f}\")\n",
+    "print(\"=\" * 48)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## \ud83d\udcc8 Live dashboard (Trackio)\n",
+    "\n",
+    "Training already streamed its reward curve to Trackio. Now we log the **eval** as its own run \u2014 `test_reward` at\n",
+    "step 0 (before) and at the final step (after) \u2014 so the before\u2192after jump shows up as a line. Then we embed the\n",
+    "whole dashboard (train + eval) right here in the notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "if USE_TRACKIO:\n",
+    "    import trackio\n",
+    "    # Log the baseline/after eval as a 2-point 'eval' run in the same project\n",
+    "    trackio.init(project=TRACKIO_PROJECT, name=\"eval\", space_id=(TRACKIO_SPACE_ID or None))\n",
+    "    trackio.log({\"test_reward\": baseline}, step=0)\n",
+    "    trackio.log({\"test_reward\": trained}, step=MAX_STEPS)\n",
+    "    trackio.finish()\n",
+    "\n",
+    "    # Embed the dashboard inline (train curves + eval line)\n",
+    "    from IPython.display import IFrame, display\n",
+    "    sub = TRACKIO_SPACE_ID.replace(\"/\", \"-\").lower()\n",
+    "    display(IFrame(f\"https://{sub}.hf.space/?project={TRACKIO_PROJECT}\", width=\"100%\", height=640))\n",
+    "else:\n",
+    "    print(\"Trackio is disabled (USE_TRACKIO = False) \u2014 skipping the dashboard.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. Push to the Hub\n",
+    "\n",
+    "We **merge the LoRA adapter into the base weights** and push a standalone model (+ processor) \u2014 so anyone can load\n",
+    "it directly with `from_pretrained`, no adapter step needed. Set `PUSH_REPO_ID` in the settings cell first and log in\n",
+    "(`huggingface-cli login` or `notebook_login()`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "if PUSH_REPO_ID:\n",
+    "    merged = trainer.model.merge_and_unload()   # fold LoRA into the base weights\n",
+    "    merged.push_to_hub(PUSH_REPO_ID)\n",
+    "    processor.push_to_hub(PUSH_REPO_ID)\n",
+    "    print(\"\u2705 pushed to https://huggingface.co/\" + PUSH_REPO_ID)\n",
+    "else:\n",
+    "    print(\"Set PUSH_REPO_ID in the settings cell to push your model.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Recap\n",
+    "\n",
+    "You evaluated a VLM, trained it with GRPO against an OpenEnv reward, measured the improvement, and pushed it.\n",
+    "\n",
+    "**Try next:** more `MAX_STEPS`, a bigger `N_EVAL_SAMPLES` for a steadier score, a different `MODEL`, or\n",
+    "`USE_LOCAL_ENV=True` to run the environment yourself."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/tests/envs/test_latex_ocr_rubric.py b/tests/envs/test_latex_ocr_rubric.py
new file mode 100644
index 000000000..6046fc9a3
--- /dev/null
+++ b/tests/envs/test_latex_ocr_rubric.py
@@ -0,0 +1,135 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Tests for the LaTeX OCR reward rubric.
+
+The rubric only depends on the standard library, so we load it directly from
+its file to keep the test fast and free of the environment's runtime deps
+(datasets, pillow, openenv server).
+"""
+
+import importlib.util
+import pathlib
+import sys
+
+import pytest
+
+
+_RUBRIC_PATH = (
+    pathlib.Path(__file__).parents[2]
+    / "envs"
+    / "latex_ocr_env"
+    / "server"
+    / "rubric.py"
+)
+_spec = importlib.util.spec_from_file_location("latex_ocr_rubric", _RUBRIC_PATH)
+rubric = importlib.util.module_from_spec(_spec)
+# Register before exec so dataclasses in the module can resolve their module.
+sys.modules[_spec.name] = rubric
+_spec.loader.exec_module(rubric)
+
+
+def test_levenshtein_basic():
+    assert rubric.levenshtein("abc", "abc") == 0
+    assert rubric.levenshtein("abc", "abd") == 1
+    assert rubric.levenshtein("", "abc") == 3
+    assert rubric.levenshtein("kitten", "sitting") == 3
+
+
+def test_normalize_collapses_whitespace():
+    assert rubric.normalize_latex("  x  ^  2  ") == "x ^ 2"
+    assert rubric.normalize_latex("a\n\tb") == "a b"
+
+
+def test_exact_match_scores_one():
+    g = rubric.LatexOCRRubric().grade("x^2 + 1", "x^2 + 1")
+    assert g.exact_match is True
+    assert g.reward == pytest.approx(1.0)
+    assert g.char_error_rate == pytest.approx(0.0)
+
+
+def test_whitespace_only_diff_is_exact():
+    # LaTeX spacing is cosmetic: "x ^ 2 + 1" == "x^2+1" after canonicalization.
+    g = rubric.LatexOCRRubric().grade("x ^ 2 + 1", "x^2+1")
+    assert g.exact_match is True
+    assert g.reward == pytest.approx(1.0)
+
+
+def test_partial_match_is_between_zero_and_exact_cap():
+    g = rubric.LatexOCRRubric(exact_weight=0.2).grade("x^2 + 2", "x^2 + 1")
+    assert g.exact_match is False
+    # partial answers are capped at (1 - exact_weight)
+    assert 0.0 < g.reward <= 0.8
+    assert 0.0 < g.char_error_rate < 1.0
+
+
+def test_empty_prediction_scores_zero():
+    g = rubric.LatexOCRRubric().grade("", "x^2 + 1")
+    assert g.reward == pytest.approx(0.0)
+    assert g.char_error_rate == pytest.approx(1.0)
+
+
+def test_empty_target_only_empty_prediction_correct():
+    r = rubric.LatexOCRRubric()
+    assert r.grade("", "").exact_match is True
+    assert r.grade("x", "").exact_match is False
+
+
+def test_default_exact_weight_is_point_four():
+    # Default 0.4 -> edit 0.6 / exact 0.4; partial answers cap at 0.6.
+    r = rubric.LatexOCRRubric()
+    assert r.exact_weight == pytest.approx(0.4)
+    g = r.grade("x^2 + 2", "x^2 + 1")  # one char off, not exact
+    assert g.exact_match is False
+    assert 0.0 < g.reward <= 0.6
+
+
+def test_exact_weight_bounds_validated():
+    with pytest.raises(ValueError):
+        rubric.LatexOCRRubric(exact_weight=1.5)
+
+
+def test_clean_latex_strips_fences_and_delimiters():
+    # The rubric cleans raw completions itself; cleaning is idempotent.
+    assert rubric.clean_latex("```latex\nx^2 + 1\n```") == "x^2 + 1"
+    assert rubric.clean_latex("$x^2 + 1$") == "x^2 + 1"
+    assert rubric.clean_latex("x^2 + 1") == "x^2 + 1"  # already clean -> unchanged
+
+
+def test_raw_fenced_completion_scored_correctly():
+    # A raw completion wrapped in a code fence still scores as an exact match.
+    g = rubric.LatexOCRRubric().grade("```latex\nx^2 + 1\n```", "x^2 + 1")
+    assert g.exact_match is True
+    assert g.reward == pytest.approx(1.0)
+
+
+def test_whitespace_padding_reward_hack_is_penalized():
+    # The core hack: correct answer + a wall of trailing whitespace. Whitespace-insensitive
+    # scoring alone would give this 1.0; the length guard must drive the reward down.
+    target = r"\frac{x^2}{2} + c"
+    r = rubric.LatexOCRRubric()
+    clean = r.grade(target, target)
+    padded = r.grade(target + "\n" * 500, target)
+    assert clean.reward == pytest.approx(1.0)  # clean answer unaffected
+    assert clean.length_factor == pytest.approx(1.0)
+    assert padded.exact_match is True  # content is still exact...
+    assert padded.reward < 0.1  # ...but the padding is punished
+    assert padded.length_factor < 0.1
+
+
+def test_normal_spacing_within_allowance_is_free():
+    # Ordinary LaTeX spacing must not trip the guard.
+    target = r"\frac{x^2}{2} + c"
+    g = rubric.LatexOCRRubric().grade(r"\frac{x^2}{2}  +  c", target)
+    assert g.length_factor == pytest.approx(1.0)
+    assert g.reward == pytest.approx(1.0)
+
+
+def test_length_guard_can_be_disabled():
+    target = r"\frac{x^2}{2} + c"
+    g = rubric.LatexOCRRubric(overlong_ratio=0).grade(target + "\n" * 500, target)
+    assert g.length_factor == pytest.approx(1.0)  # guard off -> old (hackable) behavior
+    assert g.reward == pytest.approx(1.0)