AI推理跑偏时，老师直接抢方向盘
大模型训练有个经典问题：学生模型一旦推理方向错了，后面全白费。这篇论文发现，老师模型在同样错误路径上会主动纠正方向，而学生只会一条道走到黑。研究者利用这个差异，设计了一个自动检测机制：当学生跑偏时，让老师临时接管几步，把推理拉回正轨，再交还学生继续。在8个数学推理测试中，1.7B参数的学生模型平均准确率比标准方法提升5.73%，训练计算量还减半。这不是你明天能用的工具，但揭示了AI训练中一个反直觉的优化思路——与其让学生硬扛错误，不如让老师适时介入。
📄 原文摘要(英文)
On-policy distillation (OPD) grounds token-level supervision in the student's own trajectory, yet suffers from prefix failure: once the student commits to a wrong reasoning direction, all subsequent generation builds on this deviation, producing misdirected continuations that elicit unreliable supervision and waste compute. We identify a teacher-student continuation asymmetry on failed prefixes, where the teacher tends to redirect while the student continues along the original direction, and convert it into a label-free handoff trigger in Relay On-Policy Distillation (Relay-OPD). During training, Relay-OPD constructs relay trajectories by letting the teacher briefly take over at detected trigger points to produce a teacher leg, after which the student resumes and is optimized on the resulting trajectory. A limited relay budget concentrates intervention on critical early positions while limiting departure from the student policy. With a Qwen3-4B-Instruct-2507 teacher and Qwen3-0.6B/1.7B-Non-Thinking students on eight mathematical reasoning benchmarks, Relay-OPD achieves the best or second-best results on every benchmark, outperforming standard OPD by +5.73% and the strongest baseline FastOPD by +1.49% on average for 1.7B, with consistent gains at 0.6B. Training trajectory length is reduced by over 50%.