REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning
Motivated by this finding, we introduce REWIND, a training-free controller that uses entropy dynamics to propose targeted suffix regenerations.
ProofPaper ↗
Key points
- An early mistake in the generated reasoning can constrain all subsequent reasoning.
- Repeated-generation methods, like MCMC-based approaches, attempt to improve output quality by sampling multiple generations, but their effectiveness can be limited by randomly selecting where to regenerate.
- On MATH500 with Qwen2.5-Math-7B, we find that incorrect responses have substantially higher mean token entropy than correct responses, with an average within-question relative increase of approximately 112%, suggesting that entropy can signal reasoning errors.
- These results demonstrate the effectiveness of targeted, uncertainty-guided trajectory repair across models and tasks.
Sources (1)
- [1]REWIND: Uncertainty-Guided Trajectory Repair for Test-Time ReasoningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 04:20 AM
Motivated by this finding, we introduce REWIND, a training-free controller that uses entropy dynamics to propose targeted suffix regenerations.
An early mistake in the generated reasoning can constrain all subsequent reasoning.
Extractive summary: sentences quoted from the sources.
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