[Core] Add soft thinking token budget with progressive logit bias

[efortin]

Summary

The current ThinkingTokenBudgetLogitsProcessor hard-forces </think> at exactly N tokens, regardless of where the model is in its reasoning. When the cut lands mid-sentence, the model doesn't understand it was interrupted and continues reasoning into the content field.

This PR adds a soft budget zone to the last 30% of the token budget. Instead of a single hard cut, the </think> logit is progressively boosted using an adaptive formula that measures the model's own logit distribution:

target = end_logit + 2 * gap * progress

Where gap is the real distance between the top logit and </think> at each step. This adapts to any model without hardcoded constants.

State machine

free generation --> soft zone (70-100%) --> hard force (100%, safety net)
                         |
                   </think> sampled naturally
                         |
                    content generation

• 0-70% of budget: free generation (unchanged)
• 70-100% of budget: adaptive bias ramps </think> from its natural position to above the top logit
• 100% of budget: hard force </think> (unchanged, safety net)

Key changes in builtin.py

1. Adaptive soft bias -- at each step in the soft zone, measure the actual gap between </think> and the top logit, then boost proportionally. At progress=50% </think> equals the top; at 100% it dominates.

2. Sentinel stripping -- vLLM v1 async scheduling appends -1 placeholders to output_tok_ids before sampling fills the real token. Without stripping these, the processor never detects </think> in the output and think_count is wrong.

3. Countdown skip disabled for budgeted requests -- the check_count_down optimization skips _update_think_state() for N tokens. This made think_count jump from 0 to >=budget in one step, completely skipping the soft zone.

4. State reset on all exit paths -- soft_progress is reset to 0 whenever in_think becomes False, preventing stale bias.

Files changed

File	Change
vllm/v1/sample/logits_processor/builtin.py	+67/-20 -- soft budget zone, sentinel fix, countdown fix

References

• NVIDIA NIM BudgetControlLogitsProcessor (10% grace window): https://docs.nvidia.com/nim/large-language-models/1.13.0/thinking-budget-control.html
• BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens: https://arxiv.org/html/2508.17196v1
• Original hard budget: [Feature] limit thinking tokens (hard limit) #20859

Test Plan

• pytest tests/v1/logits_processors/test_correctness.py -v (5/6 pass, 1 pre-existing failure)
• Manual: budget=10,30,50 on Qwen3-1.7B CPU -- reasoning limited, content clean, zero leak
• Manual: budget=30,40,50,60,70 on Qwen3.5-35B-FP8 H100 -- 0/5 content leaks
• Verify no regression without thinking_token_budget (soft zone inactive)

[github-actions]

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[gemini-code-assist]

Code Review

This pull request introduces a soft budget mechanism for thinking tokens, applying a progressive logit bias to end-of-thought tokens starting at 80% of the budget to encourage the model to finish naturally. A critical logic issue was identified where the existing check_count_down optimization prevents the soft budget logic from executing, rendering the feature ineffective. Feedback also suggests using constants for magic numbers and adding unit tests to verify the new behavior.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @efortin.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork