When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning
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Computer Science > Machine Learning
arXiv:2605.21606 (cs)
[Submitted on 20 May 2026]
Title:When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning
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Abstract:On-policy self-distillation (OPSD) trains a student on its own rollouts using a privileged teacher, but its standard objective weights all generated tokens equally, implicitly treating the privileged teacher target as equally reliable at every student-visited prefix. Existing entropy-based OPD methods relax this uniformity by modulating token-level supervision with teacher entropy, but high teacher entropy in reasoning has an ambiguous reliability meaning: it can reflect either non-viable uncertainty or benign solution diversity. To identify this phenomenon, we introduce a branch-viability diagnostic. Specifically, we record next-token alternatives from the privileged-answer teacher prompt, force each alternative after the student prompt plus its on-policy spine prefix, and test whether the resulting student-template continuation recovers the correct answer. On Qwen3-4B, we find that an oriented within-sequence position score is the strongest tested predictor of teacher-token reliability, reaching an area-under-ROC-curve (AUROC) of 0.83; local uncertainty scores are at most 0.57. Motivated by this trajectory-level structure, we propose Position-Weighted On-Policy Self-Distillation (PW-OPSD), which applies an increasing position weight while keeping the same student rollout, privileged teacher pass, and clipped forward-KL target as OPSD. In our comprehensive evaluations with different random seeds, the diagnostic-derived PW-OPSD improves AIME 2024 and AIME 2025 Avg@12 by +1.0 and +1.1 points, and a generalization evaluation on two larger-scale models from different families, DeepSeek-R1-Distill-Llama-8B and Olmo-3-7B-Think, also demonstrates consistent aggregate Avg@12 improvements. These results show that teacher-token reliability in reasoning distillation is trajectory-structured and can be utilized without additional teacher computation.
| Comments: | Pre-print. Code is available at this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.21606 [cs.LG] |
| (or arXiv:2605.21606v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21606
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning, by Xiaogeng Liu and 4 other authors
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