← 研踪 PaperTrailarXiv:2602.22508 · v3
Distilling Metacognitive Behaviors through Agentic Editing
首次提交 2026-02-26 08:56
版本更新 2026-10-27 15:39
雷达观测 2026-10-29 00:35
摘要
Distilling long reasoning traces from a teacher model into a smaller student also passes on the teacher's reasoning habits. Prior work and our annotation of teacher traces show that current reasoning models often start without a plan, verify superficially, abandon promising solution paths, and take unchecked shortcuts. We propose Metacognitive Behavioral Tuning (MBT), which inserts seven metacognitive behaviors into teacher traces at the points where the reasoning lacks them and then trains the student on the edited traces. Because teacher traces are long, how the edits are made matters. Rewriting a whole trace at once summarizes long traces, and choosing all insertion points at once crowds them at the start and end. Our agentic method, MBT-A, edits the trace file line by line and records each edit as a git commit, which avoids summarizing long traces and spreads the insertions. On competition math, a Qwen3.5-4B-Base student trained on MBT-A traces scores 85.4 against 83.1 for SFT on the unedited traces and also outperforms a token-matched control and five prior methods. MBT-A improves two other students as well, and the inserted behaviors clearly transfer to the students' reasoning, with rare ones several times more frequent.
作者备注：25 pages. Code and data: https://github.com/12kimih/metacognitive-behavioral-tuning
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v3 · 2026-10-27 15:39 · 最新观测版本
Distilling Metacognitive Behaviors through Agentic Editing
Distilling long reasoning traces from a teacher model into a smaller student also passes on the teacher's reasoning habits. Prior work and our annotation of teacher traces show that current reasoning models often start without a plan, verify superficially, abandon promising solution paths, and take unchecked shortcuts. We propose Metacognitive Behavioral Tuning (MBT), which inserts seven metacognitive behaviors into teacher traces at the points where the reasoning lacks them and then trains the student on the edited traces. Because teacher traces are long, how the edits are made matters. Rewriting a whole trace at once summarizes long traces, and choosing all insertion points at once crowds them at the start and end. Our agentic method, MBT-A, edits the trace file line by line and records each edit as a git commit, which avoids summarizing long traces and spreads the insertions. On competition math, a Qwen3.5-4B-Base student trained on MBT-A traces scores 85.4 against 83.1 for SFT on the unedited traces and also outperforms a token-matched control and five prior methods. MBT-A improves two other students as well, and the inserted behaviors clearly transfer to the students' reasoning, with rare ones several times more frequent.
查看此版本解读 →查看此版本原文 ↗