fix: comprehensive prompt diagnostics for debugging garbage output (#248)

Adds detailed one-time logging to help debug the persistent garbage
output issue:

1. Raw messages (role, content types, text preview) before chat template
2. Full rendered text_input (2000 chars, not 300)
3. Image metadata (mode, size, format)
4. Generation config (max_new_tokens, temperature, constrained, model type)
5. First generation output (500 chars + token count)
6. Input tensor shapes (input_ids, attention_mask, pixel_values, image_grid_thw)

The tensor shape logging is critical: if pixel_values is MISSING, the
model isn't seeing the screenshot — which would explain degenerate output
regardless of prompt correctness.

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: abrichr

openadapt_evals/training/trl_rollout.py

@@ -416,6 +416,7 @@ def make_waa_rollout_func(
     # (needs the trainer's model and processor which aren't available yet).
     _outlines_state: dict[str, Any] = {"generator": None, "attempted": False}
     _prompt_logged: list[bool] = [False]  # log the prompt once for diagnostics
+    _output_logged: list[bool] = [False]  # log first generation output
 
     def rollout_func(prompts: list[str], trainer: Any) -> dict[str, list]:
         """TRL GRPOTrainer rollout function.
@@ -517,13 +518,49 @@ def generate_fn(screenshot_bytes: bytes, instruction: str):
                 messages, tokenize=False, add_generation_prompt=True
             )
 
-            # Log the prompt on first call so operators can verify
-            # the correct format is being used (DSL, not JSON).
+            # Comprehensive prompt diagnostics on first call.
+            # This logs everything needed to debug prompt construction:
+            # raw messages, rendered text, image presence, generation config.
             if not _prompt_logged[0]:
                 _prompt_logged[0] = True
+                # 1. Raw messages (before chat template)
+                for i, msg in enumerate(messages):
+                    role = msg.get("role", "?")
+                    content = msg.get("content", "")
+                    if isinstance(content, list):
+                        types = [c.get("type", "?") for c in content]
+                        text_parts = [c.get("text", "")[:200] for c in content
+                                      if c.get("type") == "text"]
+                        logger.info(
+                            "TRL prompt msg[%d] role=%s content_types=%s "
+                            "text=%.200s",
+                            i, role, types, " ".join(text_parts),
+                        )
+                    else:
+                        logger.info(
+                            "TRL prompt msg[%d] role=%s content=%.500s",
+                            i, role, content,
+                        )
+                # 2. Rendered text (after chat template) — full prompt
+                logger.info(
+                    "TRL prompt text_input (%d chars): %s",
+                    len(text_input), text_input[:2000],
+                )
+                # 3. Image info
+                logger.info(
+                    "TRL prompt image: mode=%s size=%s format=%s",
+                    getattr(img, "mode", "?"),
+                    getattr(img, "size", "?"),
+                    getattr(img, "format", "?"),
+                )
+                # 4. Generation config
                 logger.info(
-                    "TRL rollout prompt (first 300 chars of text_input): %.300s",
-                    text_input,
+                    "TRL generation config: max_new_tokens=%d "
+                    "temperature=%s do_sample=True "
+                    "constrained=%s model_type=%s device=%s",
+                    max_new_tokens, temperature,
+                    outlines_gen is not None,
+                    type(model).__name__, device,
                 )
 
             # --- Constrained decoding path (Outlines) ---
@@ -581,6 +618,25 @@ def generate_fn(screenshot_bytes: bytes, instruction: str):
 
             text = processor.decode(completion_ids, skip_special_tokens=True)
 
+            # Log first generation output for debugging
+            if not _output_logged[0]:
+                _output_logged[0] = True
+                logger.info(
+                    "TRL first generation output (%d tokens): %.500s",
+                    len(completion_ids), text,
+                )
+                # Also log input shape for vision tensor debugging
+                logger.info(
+                    "TRL input shapes: input_ids=%s attention_mask=%s "
+                    "pixel_values=%s image_grid_thw=%s",
+                    inputs.get("input_ids", torch.tensor([])).shape,
+                    inputs.get("attention_mask", torch.tensor([])).shape,
+                    inputs.get("pixel_values", torch.tensor([])).shape
+                    if "pixel_values" in inputs else "MISSING",
+                    inputs.get("image_grid_thw", torch.tensor([])).shape
+                    if "image_grid_thw" in inputs else "MISSING",
+                )
+
             # Truncation warning — detect when output was cut off
             if len(completion_ids) >= max_new_tokens - 1:
                 logger.warning(