feat: split temperature for reasoning vs answer phase

[alexbi29]

Summary

Adds an optional reasoning_temperature sampling parameter so reasoning/thinking tokens can be sampled at a different temperature from answer/content tokens.

Example: temperature=0.0, reasoning_temperature=0.7 keeps answer generation greedy while allowing stochastic reasoning.

Usage

sampling_params = SamplingParams(
    temperature=0.0,
    reasoning_temperature=0.7,
)
llm.generate(prompts, sampling_params)

OpenAI-compatible API requests can pass the value through vllm_xargs:

{
  "temperature": 0.0,
  "vllm_xargs": {"reasoning_temperature": 0.7}
}

Implementation

• Adds SamplingParams.reasoning_temperature with validation, clamping, from_optional() support, and __repr__() output.
• Keeps the default as None, meaning no split and no extra per-step work for normal requests.
• Extracts reasoning_temperature from vllm_xargs for chat completions, completions, and responses.
• Tracks reasoning/answer phase in the V1 GPU input batch using configured reasoning start/end token IDs.
• Applies the effective temperature every decode step, after async sampled-token placeholder repair, so phase changes mid-generation are reflected before sampling.
• Routes split-temperature requests through the mixed greedy/random sampling path so answer tokens can remain greedy while reasoning tokens are random.

Fixes In Latest Update

• SamplingParams.sampling_type now considers reasoning_temperature, so seeded stochastic reasoning creates a per-request generator even when answer temperature=0.0.
• Speculative decoding now disables the split with a warning_once and falls back to the base temperature, because the current rejection-sampling path expands one request-level temperature across every draft/bonus token and cannot safely handle a phase boundary inside a speculative window.
• The per-step reasoning temperature update now runs after update_async_output_token_ids(), fixing stale phase detection under async scheduling.
• Added unit coverage for the unset sentinel, greedy reset behavior, seeded reasoning sampling type, and spec-decode fallback.

Limitations

• Only temperature is split. top_p, top_k, and min_p remain request-level parameters.
• Speculative decoding does not support per-phase temperature yet; requests with a split log a warning and use temperature for all generated tokens.
• Requires reasoning start/end token IDs from ReasoningConfig; without them, the split path is a no-op.

Duplicate-Work Check

This updates existing PR #3 rather than opening another PR. I checked upstream open PRs with these searches:

• reasoning_temperature: no open PRs found.
• "reasoning temperature": no exact duplicate found.
• thinking temperature: no exact duplicate found; related PR [SpecDec + Reasoning] Fix race condition when <channel|> reasoning-end vllm-project/vllm#43691 handles a speculative decoding + reasoning-end race, not per-phase temperature control.

Testing

• git diff --check: passed.
• .venv/bin/python -m pytest tests/v1/sample/test_reasoning_temperature.py -q: not run because .venv/bin/python is missing in this checkout.
• pre-commit: not run because pre-commit is not installed.
• uv: not run because uv is not installed.

AI Assistance

AI assistance was used to implement and review parts of this change. The submitting human should review every changed line and run the relevant tests before sending upstream.

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[alexbi29]

Update: per-step redesign pushed (fcb82e1d) — remaining work

The previous revision did not apply reasoning_temperature at runtime: the blend was in _make_sampling_metadata(), which only runs on batch-composition changes, so the think-mask was computed once at admission (output empty → all-False) and the effective temperature was frozen at the answer temperature for the whole generation. Confirmed on gemma-4-26B via per-token logprob (off-argmax) analysis — determinism-independent, since --async-scheduling makes even temp=0 non-deterministic here.

This push moves the blend to a per-decode-step InputBatch.update_reasoning_temperature() and fixes the unset-default gate. Verified working: reasoning off-argmax moves 2.6% (rt=0) → 8.4% (rt=1) while the answer phase stays near the greedy floor.

What's done

• Per-step temperature blend (where(in_think, reasoning_temperature, temperature)), gated on has_reasoning_temp_split (zero overhead when unused).
• reasoning_temperature defaults to None ("mirror temperature") instead of 1.0, so ordinary requests no longer get forced onto the mixed greedy+random path.
• has_reasoning_temp_split derived from a per-request set (clears when split requests leave the batch).
• Plumbed through chat/completion/responses to_sampling_params() via vllm_xargs, without mutating the request.
• Removed dead SamplingMetadata.reasoning_temperature / think_mask fields.
• tests/v1/sample/test_reasoning_temperature.py (SamplingParams contract).

Remaining work

• Incremental think-state scan. _update_think_state currently rescans the full output each step — O(output_len) per split row per step. A cursor-based incremental scan is unsafe under --async-scheduling: the in-flight output slot holds a placeholder -1 that's overwritten with the real token id one step later, so a length cursor skips the real marker (this exact bug silently no-op'd the first incremental attempt). A correct version must track the last committed output length.
• Speculative decoding. The mask is one token lagged and per-request; verify behavior with --speculative-config (draft + bonus tokens, phase transition inside a draft window). No crash seen, but correctness at the boundary is unverified.
• First-class API surface. reasoning_temperature is only reachable via top-level vllm_xargs ({"vllm_xargs": {"reasoning_temperature": 1.0}}). The OpenAI Python client's extra_body={"vllm_xargs": {...}} does not populate the field — document this, or expose reasoning_temperature as a real request field.
• Implicit think-start models. Detection requires the start marker to appear in prompt or output (last_start > last_end). Models that begin reasoning with no explicit start token are treated as answer-phase from token 0.
• One-token boundary latency. At the think→answer transition the mask is based on output through the previous step, so the first answer token may be sampled under the reasoning temperature. Acceptable in practice; document.
• Other sampling params unified. top_p / top_k / min_p / penalties are not split per phase (intentional for now; noted in the design doc as a future extension).
• Tests + CI. Add an integration test that exercises the per-step switch end-to-end (current tests only cover SamplingParams validation) and a perf-regression test confirming zero overhead when reasoning_temperature is unset. Run pre-commit (ruff + mypy) as CI would.

AI assistance (Claude) was used for this analysis and the redesign; a human must review every line and run the relevant tests before merge.