文章背景与核心概要
大型语言模型（LLM）在处理复杂的推理任务时往往会遇到瓶颈，这通常不是因为缺乏基础能力，而是由于其中间处理步骤中存在局限性的“推理缺陷（reasoning bugs）”。本文引入了一种名为啄木鸟蒸馏（Woodpecker Distillation）的新型弱到强（weak-to-strong）训练框架，旨在精准识别并修正这些缺陷。
研究人员证明，这些推理错误通常可以通过插入由“弱”探测模型生成的简短补丁来进行修复。该方法没有简单地对这些补丁进行微调，而是利用对比局部干预构建了一个纠正性的教师分布。通过将该信号蒸馏到强模型中，该框架有效地重塑了模型的推理轨迹，从而在数学推理基准测试中带来了持续的性能提升。
Woodpecker Distillation: Weak Models Diagnose Reasoning Bugs in Strong Models
arXiv: 2608.05168
Date: May 27, 2026
Authors: Dayu Wang, Jiaye Yang, Weikang Li, Jiahui Liang, Yang Li, Deguo Xia, Jizhou Huang
arXiv: 2608.05168
Date: May 27, 2026
Authors: Dayu Wang, Jiaye Yang, Weikang Li, Jiahui Liang, Yang Li, Deguo Xia, Jizhou Huang
Summary
Large Language Models (LLMs) often struggle with complex reasoning tasks, not due to a lack of fundamental capability, but because of localized "reasoning bugs" in their intermediate processing steps. This paper introduces Woodpecker Distillation, a novel weak-to-strong training framework designed to identify and correct these bugs.
The authors demonstrate that these reasoning errors are often repairable by inserting short patches generated by a "weak" probe model. Rather than simply fine-tuning on these patches, the method uses contrastive local interventions to construct a corrective teacher distribution. By distilling this signal into the strong model, the framework effectively reshapes the model's reasoning trajectory, leading to consistent performance improvements on mathematical reasoning benchmarks.
Summary
Large Language Models (LLMs) often struggle with complex reasoning tasks, not due to a lack of fundamental capability, but because of localized "reasoning bugs" in their intermediate processing steps. This paper introduces Woodpecker Distillation, a novel weak-to-strong training framework designed to identify and correct these bugs.
The authors demonstrate that these reasoning errors are often repairable by inserting short patches generated by a "weak" probe model. Rather than simply fine-tuning on these patches, the method uses contrastive local interventions to construct a corrective teacher distribution. By distilling this signal into the strong model, the framework effectively reshapes the model's reasoning trajectory, leading to consistent performance improvements on mathematical reasoning benchmarks.
Key Concepts
The Nature of Reasoning Bugs
The research posits that strong models possess the latent capacity to solve complex problems but frequently derail due to minor, localized errors. These errors are not indicative of global incompetence but act as "bugs" that, if corrected early, allow the model to reach the correct conclusion.
Key Concepts
The Nature of Reasoning Bugs
The research posits that strong models possess the latent capacity to solve complex problems but frequently derail due to minor, localized errors. These errors are not indicative of global incompetence but act as "bugs" that, if corrected early, allow the model to reach the correct conclusion.
The Woodpecker Distillation Framework
The proposed method moves beyond simple imitation learning by focusing on the distributional shift caused by interventions: 1. Contrastive Intervention: The system compares successful and unsuccessful patches generated by a weak model at the same reasoning prefix. 2. Teacher Distribution: It constructs a corrective teacher distribution based on the future token predictions induced by these patches. 3. Distillation: This refined signal is distilled into the strong model, teaching it to avoid or correct its own reasoning pitfalls.
The Woodpecker Distillation Framework
The proposed method moves beyond simple imitation learning by focusing on the distributional shift caused by interventions: 1. Contrastive Intervention: The system compares successful and unsuccessful patches generated by a weak model at the same reasoning prefix. 2. Teacher Distribution: It constructs a corrective teacher distribution based on the future token predictions induced by these patches. 3. Distillation: This refined signal is distilled into the strong model, teaching it to avoid or correct its own reasoning pitfalls.
Experimental Results
Experiments conducted on standard mathematical reasoning benchmarks demonstrate that: * Performance Gains: Woodpecker Distillation consistently improves the accuracy of strong models. * Superiority: The method outperforms traditional direct imitation baselines, proving that learning from the process of correction is more effective than simply mimicking the output of a correction.
Experimental Results
Experiments conducted on standard mathematical reasoning benchmarks demonstrate that: * Performance Gains: Woodpecker Distillation consistently improves the accuracy of strong models. * Superiority: The method outperforms traditional direct imitation baselines, proving that learning from the process of correction is more effective than simply mimicking the output of a correction.
Access & Resources
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Access & Resources
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)