ReDiST 提出按学生条件构建推理轨迹的蒸馏方法
Beyond the Answer: Student-Conditioned Trace Construction for Reasoning Distillation
蒸馏推理模型时，与其给标准答案的思路，不如从学生自己的错误草稿修出一条它能学会的路径，这篇论文就是这么做的。
arXiv 论文提出 ReDiST，一种面向学生模型的推理轨迹构建与蒸馏框架。方法从学生自身的尝试出发，对答对的草稿按证据修订，对答错的草稿借助验证答案进行修复。由冻结的学生匹配阅读器用答案恢复率和验证答案概率对候选轨迹打分筛选。蒸馏时通过辅助答案损失平衡轨迹监督与最终答案监督。在问答基准上的实验显示，ReDiST 在源任务和跨基准迁移上均优于仅答案监督、原始轨迹以及被评测的轨迹筛选与改写基线。
Beyond the Answer: Student-Conditioned Trace Construction for Reasoning Distillation
A reasoning trace can accompany a correct answer without providing effective supervision for a particular student. We study how to construct traces that improve learning beyond answer-only supervision by considering both their intermediate content and their accessibility to the target learner. We introduce ReDiST, a student-conditioned trace-construction and distillation framework for evidence-rich reasoning. Starting from the student's own attempts, ReDiST revises answer-correct drafts against the supplied evidence and repairs unsuccessful drafts with additional guidance from the verified answer. A frozen student-matched reader screens and ranks original and edited candidates using answer recovery and verified-answer probability, given the question, evidence, and candidate trace. We distill the selected targets while balancing trace and final-answer supervision with an auxiliary answer loss. Construction and selection occur offline, and inference uses only the distilled student. Experiments on question-answering benchmarks show that ReDiST can improve both source-task and cross-benchmark performance over answer-only, original-trace, and the evaluated trace-selection and rewriting baselines. Across supervision recipes, performance rankings can differ between source and transfer benchmarks, highlighting the importance of evaluating trace teaching utility across distributions.