有效性校准的推理蒸馏：让大语言模型向小模型更高效地传递多步推理
Validity-Calibrated Reasoning Distillation
AI 导读
一项 arXiv 研究提出“有效性校准的推理蒸馏”框架，把推理蒸馏视为局部学习信号分配问题，而非静态师生层级下的轨迹模仿。方法在同一前缀下比较学生与教师提出的下一步动作，用相对局部有效性调节蒸馏更新强度，形成动态、依赖上下文的监督。该框架在数学、代码和指令跟随基准上均超过强蒸馏基线。
正文 · 原文
Abstract:Reasoning distillation aims to transfer multi-step reasoning capabilities from large language models to smaller, efficient ones. While recent methods have shown promising gains, they typically rely on static teacher-student hierarchies and frame distillation as trajectory imitation. This is misaligned with the structure of reasoning, where intermediate steps are locally under-specified: global correctness constrains the final answer, but does not uniquely determine each intermediate move. We propose validity-calibrated reasoning distillation, a framework that treats reasoning distillation as a problem of local learning-signal allocation rather than path alignment. Instead of enforcing token-level imitation, we compare the student's and teacher's proposed next-step actions under the same prefix and use their relative local validity to modulate the strength of the distillation update. This yields a dynamic, context-dependent supervision that preserves the teacher's structural guidance while adapting update strength to local reasoning quality. Across math, code, and instruction-following benchmarks, our method outperforms strong distillation baselines. These results indicate that effective LLM reasoning distillation is governed not by rigid trajectory imitation, but by locally calibrated allocation of learning signal.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.04078 [cs.LG] |
| (or arXiv:2605.04078v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.04078 arXiv-issued DOI via DataCite |
Submission history
From: Khouloud Saadi [view email]
[v1]
Tue, 14 Apr 2026 12:32:12 UTC (8,139 KB)
[v2]
Sat, 9 May 2026 13:58:28 UTC (9,341 KB)
[v3]
Thu, 29 Oct 2026 08:28:26 UTC (8,133 KB)
来源：arXiv cs.AI · arxiv.org