arXiv:2605.11458v3 Announce Type: replace-cross Abstract: On-policy self-distillation has become a strong recipe for LLM reasoning, where a privileged teacher supervises the student's own rollouts while conditioning on the reference solution. A design choice shared by nearly all such methods, however, has gone unquestioned: the teacher always sees the full reference reasoning. We argue that this default itself is part of the problem and identify a teacher-side exposure mismatch: when the teacher conditions on reasoning far beyond the student's current competence, the resulting token targets be
This paper represents a new iteration in ongoing research into optimizing large language models for reasoning, addressing a fundamental teacher-student interaction problem in self-distillation.
Improved self-distillation techniques can significantly enhance LLM reasoning capabilities and efficiency, impacting a wide array of AI applications and potentially lowering their operational costs.
The focus on adaptive teacher exposure for self-distillation introduces a more nuanced approach to training LLMs, moving beyond static, full-reference supervision for potentially superior outcomes.
- · AI researchers
- · LLM developers
- · Cloud AI providers
- · Enterprises adopting AI
- · Developers relying on less efficient distillation methods
- · AI models with suboptimal reasoning capabilities
More capable and efficient large language models become broadly available.
This could accelerate the deployment of advanced AI agents and automation across industries.
It might further democratize access to sophisticated AI reasoning, leading to new unforeseen applications and business models.
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