arXiv Artificial Intelligence

OISD: On-Policy Internal Self-Distillation of Language Models

10 days ago 01.09.2026 07:00 👁 1 views
OISD: On-Policy Internal Self-Distillation of Language Models

Quick summary

arXiv:2605.29089v2 Announce Type: replace-cross Abstract: Recent reinforcement learning (RL) post-training approaches primarily optimize the final output policy using sparse outcome-level rewards, while largely overlooking predictive signals encoded in intermediate representations. In this paper, we introduce a new paradigm called on-policy internal self-distillation and propose the OISD framework, which improves reasoning by transferring on-policy predictive signals from the final layer to intermediate representations. During rollout and Group Relative Policy Optimization (GRPO) optimization,

Key takeaways

  • arXiv:2605.29089v2 Announce Type: replace-cross Abstract: Recent reinforcement learning (RL) post-training approaches primarily optimize the final output policy using sparse outcome-level rewards, while largely overlooking predictive signals encoded in intermediate representations.
  • In this paper, we introduce a new paradigm called on-policy internal self-distillation and propose the OISD framework, which improves reasoning by transferring on-policy predictive signals from the final layer to intermediate representations.
  • During rollout and Group Relative Policy Optimization (GRPO) optimization,

Why it matters

“OISD: On-Policy Internal Self-Distillation of Language Models” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗