fix: only send reasoning.effort to models that support it by shehabyasser-scale · Pull Request #54 · scaleapi/vero

shehabyasser-scale · 2026-07-25T03:58:12Z

Why this is now load-bearing, not defensive

This started as cleanup for a 400 seen during a local gpt-4o swap. It is now a prerequisite, because gpt-4o is the only usable eval model on the configured Azure resource.

Probing the endpoint directly, only two deployments answer: gpt-5.3-codex and gpt-4o. Everything else, including the committed gpt-5.4-mini-2026-03-17 that four of the five benchmarks point at, returns 404 DeploymentNotFound (details in #58). So moving swe-atlas-qna to gpt-4o is the only way to get it running at all, and without this guard every one of its agent calls trades a 404 for:

400 unsupported_parameter: 'reasoning.effort' is not supported with this model

The change

Each of the five bench agents built an unconditional "reasoning": {"effort": ...} into its Responses request. Now it is sent only when the model supports it:

_model = self._api_model.lower()
if _model.startswith(("gpt-5", "o1", "o3", "o4")) or "codex" in _model:
    request["reasoning"] = {"effort": "high"}

The predicate runs on _api_model, which is model_name.removeprefix("openai/") (e.g. atlas_agent/agent.py:80), so the openai/ prefix carried in build.yaml never defeats the startswith.

Both branches matter and both are covered by the run below:

gpt-4o → no reasoning key → the 400 cannot occur.
gpt-5.3-codex (producer) → matches on "codex" → still receives effort. gpt-5.4-mini-2026-03-17 → matches on gpt-5. So no reasoning model silently loses its effort setting.

Verification

Live combined run in flight on swe-atlas-qna with a gpt-4o agent, on a branch carrying this plus #51/#52/#53. The check is session/artifacts/inference/requests/*.jsonl: zero records with status 400 / unsupported_parameter, against 322/322 upstream failures in the last run. Posting the counts here before merge.

Update: rebased on the refreshed base, swe-bench-pro hunk dropped

The base branch (#50) now applies this exact capability gate to the swe-bench-pro agent itself, as part of fixing five seed-agent defects there. That file therefore conflicted.

Resolved by merging the refreshed base and taking its version of swebench_pro_agent/agent.py verbatim. The gate is still applied to swe-bench-pro, just by the base commit rather than by this PR, so the merged result is unchanged and this PR's diff drops from six files to five. The content changes to the five surviving agents are byte-identical to what was reviewed.

This also resolves Greptile's note about swe-bench-pro inlining the predicate: the base's version uses the shared _is_reasoning_model helper.

Verified: all five remaining hunks apply cleanly to the current pr3-harness-bench tip.

Note on officeqa, do not collapse the two guards

A hand-reapplied copy of this gate currently running in a local worktree collapses both settings under one capability check:

if _is_reasoning_model(self._api_model):
    kwargs["reasoning_effort"] = "medium"
    kwargs["parallel_tool_calls"] = False   # regression

That is a behavior regression and this PR deliberately does not do it. parallel_tool_calls is a separate axis: Fireworks-served models reject it but gpt-4o supports it, so it stays a provider check while reasoning_effort moves to a capability check. Collapsing them silently drops parallel_tool_calls=False for gpt-4o.

Overlap with #61

#61 (browsecomp-plus truncated-answer fix) also touches this agent's _completion_kwargs and necessarily carries the same gate, because it parameterizes the same line. Whichever lands second needs that hunk dropped.

Greptile Summary

This PR replaces unconditional reasoning.effort / reasoning_effort parameters with a capability gate (_is_reasoning_model) across five bench agents, fixing 400 errors when running against gpt-4o on Azure. It also switches non-reasoning models from max_tokens to the correct max_completion_tokens parameter name where appropriate.

Four agents (browsecomp-plus, officeqa, swe-atlas-qna, tau3) define a shared _is_reasoning_model module-level helper and use it for both the token-limit key selection and the reasoning_effort gate.
gaia correctly fixes the reasoning.effort guard but inlines the same predicate logic rather than defining the helper, creating a potential drift point if the predicate evolves.
officeqa correctly splits the former combined fireworks check into two independent guards: _is_reasoning_model for reasoning_effort and \"fireworks\" not in model for parallel_tool_calls, which is an intentional behavior preservation noted in the PR description.

Confidence Score: 5/5

Safe to merge — the fix is straightforward and eliminates a live 400 error when using gpt-4o on Azure.

All five agents now correctly skip reasoning.effort for non-reasoning models. The logic is simple, well-commented, and the predicate matches the documented model naming conventions. The only wrinkle is that gaia inlines the predicate rather than using the shared helper, but the logic is identical today and causes no current breakage.

Files Needing Attention: harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py — uses an inline predicate instead of the _is_reasoning_model helper defined in every other changed file.

Important Files Changed

Filename	Overview
harness-engineering-bench/browsecomp-plus/baseline/target/src/browsecomp_plus_agent/agent.py	Adds _is_reasoning_model helper; switches reasoning_effort gate and max_tokens→max_completion_tokens selection to capability-based check. Clean change.
harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py	Correctly gates reasoning.effort on the Responses API; uses an inline predicate instead of a shared _is_reasoning_model helper, creating a future drift risk.
harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/agent.py	Adds _is_reasoning_model helper; correctly separates the capability check for reasoning_effort from the provider check for parallel_tool_calls. Clean change.
harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py	Adds _is_reasoning_model helper and switches the reasoning_effort gate from a provider string check to a capability check. This is the primary target of the fix.
harness-engineering-bench/tau3/baseline/target/src/tau3_agent/agent.py	Adds _is_reasoning_model helper; switches gate and token-key selection correctly. Both calls to _is_reasoning_model happen inside the 80-turn loop but the function is trivially cheap.

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A[Agent._api_model] --> B{_is_reasoning_model?}
    B -->|"name.startswith('gpt-5','o1','o3','o4')\nor 'codex' in name"| C[YES]
    B -->|otherwise| D[NO]

    C --> E["token key = max_completion_tokens"]
    D --> F["token key = max_tokens"]

    C --> G["add reasoning_effort / reasoning.effort"]
    D --> H["omit reasoning param"]

    E & F --> I[Build API request]
    G & H --> I

    I --> J{"officeqa only:\n'fireworks' not in model?"}
    J -->|YES| K["add parallel_tool_calls=False"]
    J -->|NO| L[omit parallel_tool_calls]
    K & L --> M[Submit API call]

_{Reviews (6): Last reviewed commit: "fix: pick the token-limit parameter by m..." | Re-trigger Greptile}

shehabyasser-scale · 2026-07-25T06:35:20Z

Validated live, with a same-model A/B

Both halves of the guard are now confirmed against real runs, using the same agent model on both sides so the only variable is this patch.

Without the guard — GAIA, 2026-07-24, gpt-4o agent

runs/gaia-full/jobs/2026-07-24__18-09-28, request-log census:

1274 × (evaluation,   gpt-4o, 200)
 514 × (evaluation,   gpt-4o, 400)   <- unsupported_parameter: reasoning.effort
  79 × (evaluation,   gpt-4o, 500)
 296 × (finalization, gpt-4o, 200)
 201 × (finalization, gpt-4o, 400)   <- same
  12 × (finalization, gpt-4o, 500)
 152 × (producer, gpt-5.3-codex, 200)

715 HTTP 400s — roughly 30% of every inference call that run made, thrown away on a parameter the model does not accept.

With the guard — swe-atlas-qna, 2026-07-25, gpt-4o agent

runs/atlas-all-fixes/jobs/2026-07-25__08-58-16, same census:

284 × (evaluation, gpt-4o,        200)
147 × (producer,   gpt-5.3-codex, 200)

Zero 400s. Zero non-200 responses of any kind. usage.json agrees: evaluation: 284 requests / 0 upstream_errors / 245,524 tokens.

The negative case is confirmed too

The predicate must not quietly strip effort from models that want it. Two independent confirmations:

Live: the 2026-07-25 07:33 run was already running guarded candidate code at gpt-5.4-mini-2026-03-17, and all 322 of its evaluation requests still carried reasoning: {effort: medium} — startswith("gpt-5") matches after the prefix strip.
In this run: the producer is gpt-5.3-codex, which matches on "codex", and its 147 requests all succeeded.

So: non-reasoning models lose the parameter, reasoning models keep it.

One thing worth flagging for review

The guard is five copy-pasted lines in five separate bench agents, with no shared helper and no unit test — the evidence above is all end-to-end. That is a fair reviewer objection. I did not refactor it into a helper because these files are the candidate programs under optimization: the optimizer edits them, and a shared import outside the candidate repo would change what the candidate can see and modify. Worth a deliberate decision rather than an accident, though.

This is also no longer optional. Per #58, gpt-4o and gpt-5.3-codex are the only two deployments that answer on the configured Azure resource, so every benchmark that gets un-blocked has to run a non-reasoning agent, and each one 400s on every call without this.

shehabyasser-scale · 2026-07-26T06:30:31Z

Restacked and narrowed from five agents to two.

b7a5b80 added a gate to officeqa, tau3, swe-atlas-qna and browsecomp-plus, so three of my five hunks conflicted with it. I have dropped those three rather than rewriting them, and kept only gaia and swe-bench-pro, which have no gate at all. gaia still sends "reasoning": {"effort": "medium"} unconditionally at gaia_agent/agent.py:188.

One thing to flag rather than change unilaterally, since it is your code and a judgement call:

if "fireworks" not in self._api_model:
    kwargs["reasoning_effort"] = "medium"

That gate is provider-shaped, not capability-shaped. It correctly spares Fireworks-served open models, but it still sends reasoning_effort to any non-Fireworks model that is not a reasoning model, Azure gpt-4o being the case I measured. The capability form is what my remaining hunks use:

_model = self._api_model.lower()
if _model.startswith(("gpt-5", "o1", "o3", "o4")) or "codex" in _model:

which also excludes fireworks_ai/*, so it is a strict superset of the current behaviour. If you want, the clean merge is to keep your provider gate for parallel_tool_calls (gpt-4o does support that, so the capability gate would wrongly drop it) and use the capability gate for reasoning_effort alone. I did not make that change here because it is your gate and either reading is defensible.

Live measurement behind the PR, unchanged: gpt-4o without a guard on gaia burned 715 HTTP 400s of 2376 calls (514 evaluation + 201 finalization); gpt-4o with the guard on swe-atlas-qna logged 284 x (evaluation, gpt-4o, 200) and 147 x (producer, gpt-5.3-codex, 200), zero non-200s.

Related and separate, worth its own look: gaia is the one benchmark now pointed at a Fireworks model while still on the Responses API with a hosted web_search tool, parallel_tool_calls: False, and a bare AsyncOpenAI() with no max_retries=8. That is the combination 7d57386's message says does not translate via litellm.

Full suite matches base: 11 pre-existing failures, none new.

shehabyasser-scale · 2026-07-26T08:46:12Z

Restoring the three hunks I dropped, because the gap turned out to be load-bearing rather than stylistic. I said above that the provider gate "will bite whoever points this at OpenAI or Azure". It bit today, and it is worse than I described: it makes three of the five benchmarks unrunnable on any non-Fireworks upstream.

Measured against the configured Azure endpoint, running the swe-atlas-qna seed agent over its full 50-case held-out split:

target model	gate	result
`gpt-5.3-codex`	provider	50/50 `BadRequestError`, `400 The requested operation is unsupported`
`gpt-4o`	provider	50/50 `BadRequestError`, `400 Unrecognized request argument supplied: reasoning_effort`
`gpt-4o`	capability	0 `BadRequestError`, agent inference succeeded

Two separate things conspire, and the second is the one worth knowing:

gpt-4o is not Fireworks, so the guard sends it reasoning_effort, and it 400s.
gpt-5.3-codex cannot be the escape hatch, because it has no /chat/completions surface on this resource at all. Probed directly: 400 for every parameter combination, including a bare request with only max_completion_tokens. It answers on /responses only. Since 1b5ee0c / 7d57386 ported these three agents to Chat Completions, no reasoning-capable model on this endpoint can drive them.

So the intersection of "model this endpoint serves on Chat Completions" and "model the provider gate does not break" is empty. The capability gate is the only thing that makes swe-atlas-qna, tau3 and officeqa runnable here.

The change is a strict superset of current behaviour: fireworks_ai/* names do not match gpt-5/o1/o3/o4/codex either, so nothing changes for the default configuration.

officeqa needed care. It gates two arguments on the one condition. Only reasoning_effort moves to the capability check; parallel_tool_calls stays provider-gated, because that one really is a Fireworks-vs-OpenAI distinction and gpt-4o supports it. Collapsing both would have silently changed gpt-4o's tool-calling behaviour.

Verified after the change: all three files compile, and the follow-on run reached the verifier stage with zero BadRequestError, so the remaining failure there is a separate issue (the in-container rubric judge needs upstream credentials, which task_services_use_upstream supplies under vero and --ve supplies under raw harbor).

varunursekar · 2026-07-27T00:49:18Z

+    open models match none of these prefixes, so they keep the legacy shape.
+    """
+    name = model.lower()
+    return name.startswith(("gpt-5", "o1", "o3", "o4")) or "codex" in name


is this something we should include in all the target agent designs? the only task for which we are going to use a reasoning model as the target is gaia

gaia and swe-bench-pro send `reasoning: {effort: ...}` unconditionally. A non-reasoning model rejects it with HTTP 400, so every call fails and the case is scored as an honest-looking task failure. Measured on a live gaia run: 715 of 2376 calls returned 400 (514 evaluation, 201 finalization). Gate it on the model being reasoning-capable. No behavior change for reasoning models; gpt-4o stops 400ing. Scoped to the two agents that have no gate at all. officeqa, tau3 and swe-atlas-qna gained `if "fireworks" not in self._api_model` in b7a5b80, so their hunks are dropped here rather than rewritten. That gate is provider-shaped rather than capability-shaped and still sends reasoning_effort to any non-Fireworks non-reasoning model such as Azure gpt-4o, which is raised as review on the PR rather than changed unilaterally. Refs #51. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

Restores the hunks dropped when this PR was restacked. b7a5b80 added `if "fireworks" not in self._api_model` to officeqa, tau3 and swe-atlas-qna, which is provider-shaped rather than capability-shaped: it correctly spares Fireworks-served open models and still sends `reasoning_effort` to any non-Fireworks model that cannot accept it. That is not theoretical. Measured today against the configured Azure endpoint, running the swe-atlas-qna seed agent over its 50-case held-out split: gpt-5.3-codex, provider gate 50/50 BadRequestError 400 "The requested operation is unsupported" (that model has no chat/completions surface on this resource at all) gpt-4o, provider gate 50/50 BadRequestError 400 "Unrecognized request argument supplied: reasoning_effort" gpt-4o, capability gate 0 BadRequestError, agent inference succeeded So the provider gate makes these three benchmarks unrunnable against any non-Fireworks upstream, which is every endpoint we currently have. The capability form also excludes `fireworks_ai/*`, so it is a strict superset of the current behaviour and changes nothing for the default configuration. officeqa gates two arguments on that one condition. Only `reasoning_effort` moves: `parallel_tool_calls` is a separate axis that Fireworks rejects and gpt-4o supports, so it keeps the provider check. Refs #51. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

The four Chat Completions agents send `max_tokens` unconditionally. Every gpt-5 model rejects it: "Unsupported parameter: 'max_tokens' is not supported with this model. Use 'max_completion_tokens' instead." So even with the reasoning_effort gate corrected, these agents could not target any modern OpenAI reasoning model, only Fireworks-served models and gpt-4o. Select the parameter name from the same capability test, and fold the two copies of that test into one `_is_reasoning_model` helper per agent so they cannot drift apart. browsecomp-plus's reasoning_effort check moves onto the helper as well; it had the same provider-shaped gate as the other three. Verified live against the configured Azure endpoint, sending exactly the shapes the patched agents now build: gpt-4o ['max_tokens'] 200 gpt-5.4-mini-2026-03-17 ['max_completion_tokens','reasoning_effort'] 200 and the predicate classifies the suite's own targets correctly: `fireworks_ai/deepseek-v4-flash` and `fireworks_ai/gpt-oss-120b` are both non-reasoning, so the default configuration keeps the legacy shape and is unaffected. gaia and swe-bench-pro are untouched here: they use the Responses API, whose `max_output_tokens` has no such split. Refs #51. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

shehabyasser-scale force-pushed the feat/swe-bench-pro-baseline-scaffold branch from ccc7d07 to ec708e9 Compare July 26, 2026 06:28

shehabyasser-scale force-pushed the fix/judge-reasoning-effort branch from 28085f3 to 4c0cd19 Compare July 26, 2026 06:29

shehabyasser-scale mentioned this pull request Jul 26, 2026

3/3 Add harness-engineering-bench (GAIA + benchmarks) #46

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varunursekar reviewed Jul 27, 2026

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shehabyasser-scale mentioned this pull request Jul 27, 2026

fix: stop submitting truncated reasoning as the browsecomp-plus answer #61

Open

shehabyasser-scale and others added 3 commits July 28, 2026 06:52

shehabyasser-scale force-pushed the feat/swe-bench-pro-baseline-scaffold branch from ac3aee3 to a4e956a Compare July 28, 2026 04:12

shehabyasser-scale force-pushed the fix/judge-reasoning-effort branch from 15ad941 to c156c59 Compare July 28, 2026 04:12

shehabyasser-scale merged commit 2aff9f4 into feat/swe-bench-pro-baseline-scaffold Jul 28, 2026
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fix: only send reasoning.effort to models that support it - #54

fix: only send reasoning.effort to models that support it#54
shehabyasser-scale merged 3 commits into
feat/swe-bench-pro-baseline-scaffoldfrom
fix/judge-reasoning-effort

shehabyasser-scale commented Jul 25, 2026 •

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shehabyasser-scale commented Jul 25, 2026 • edited by greptile-apps Bot Loading Uh oh! There was an error while loading. Please reload this page.

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Why this is now load-bearing, not defensive

The change

Verification

Update: rebased on the refreshed base, swe-bench-pro hunk dropped

Note on officeqa, do not collapse the two guards

Overlap with #61

Greptile Summary

Confidence Score: 5/5

Important Files Changed

Flowchart

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shehabyasser-scale commented Jul 25, 2026

Validated live, with a same-model A/B

Without the guard — GAIA, 2026-07-24, gpt-4o agent

With the guard — swe-atlas-qna, 2026-07-25, gpt-4o agent

The negative case is confirmed too

One thing worth flagging for review

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shehabyasser-scale commented Jul 26, 2026

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shehabyasser-scale commented Jul 25, 2026 •

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