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.
实验结果
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.
Motivated by this finding, we introduce REWIND, a training-free controller that uses entropy dynamics to propose targeted suffix regenerations.
Across three model families and five benchmarks, REWIND outperforms all baselines, with gains of 0.51-10.00 percentage points over the strongest baseline.
These results demonstrate the effectiveness of targeted, uncertainty-guided trajectory repair across models and tasks.
Our code is available at https://anonymous.4open.science/r/rewind-D310.