When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning
arXiv:2605.21606v1 Announce Type: new 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
The paper addresses a critical challenge in on-policy self-distillation for reasoning, a technique central to improving AI model efficiency and performance, at a time of intense focus on AI scalability.
Improved self-distillation methods can significantly enhance the reliability and performance of AI models, accelerating the development of more capable and robust AI agents.
The proposed position-weighted on-policy self-distillation offers a more nuanced approach to teacher token reliability, potentially leading to more efficient and effective AI training strategies.
- · AI researchers
- · Developers of large language models
- · AI-driven product companies
- · AI models with inefficient training mechanisms
More accurate and robust AI models, particularly in reasoning tasks.
Faster development and deployment of advanced autonomous AI agents across various industries.
Enhanced automation and capability in complex decision-making systems, potentially impacting professional white-collar workflows.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG