Pass the Baton: Trajectory-Relayed On-Policy Distillation
Score
78提出了前缀失败问题的无标签解决方案，在在线蒸馏领域有显著信息增量。
论文结构完整，问题定义清晰，实验设计严谨，结果对比充分。
方法可降低训练成本并提升小模型推理能力，对工业界蒸馏实践有直接价值。
论文为最新工作，且解决的是当前大模型蒸馏中的热点问题。
arXiv预印本，来自AI/CL/LG分区，且作者方可能为知名机构。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The method is innovative, experiments are thorough, practical value is high, with consistent improvements across multiple benchmarks.
AI commentary
「This paper is especially valuable for researchers working on model compression and distillation. It hits the pain point of on-policy distillation—prefix error accumulation—and cleverly exploits teacher-student behavior differences as trigger signals without extra labeling. If you are improving small model reasoning capabilities, this offers a low-cost and effective new approach.」
Quick overview
This paper proposes Relay-OPD, an improved on-policy distillation method that addresses the prefix failure problem where the student model commits to wrong reasoning directions. By detecting failed prefixes and letting the teacher model briefly take over to produce a 'teacher leg', then resuming student generation for optimization, it effectively corrects erroneous trajectories. On mathematical reasoning benchmarks, the 1.7B model outperforms standard OPD by 5.73% on average, with training trajectory length reduced by over 50%.
- Identifies teacher-student continuation asymmetry on failed prefixes: teachers tend to redirect while students continue along the original direction.
- Converts this asymmetry into a label-free handoff trigger for constructing relay trajectories.
- Limited relay budget concentrates intervention on critical early positions while limiting departure from the student policy.
- On 8 mathematical reasoning benchmarks, 1.7B model outperforms standard OPD by 5.73% and the strongest baseline FastOPD by 1.49% on average.
Full text
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