arXiv:2605.29089v1 Announce Type: new 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, the final
The continuous push for more efficient and robust language models necessitates novel post-training methods that move beyond sparse reward optimization, driving research into internal predictive signals.
Improving the reasoning capabilities of large language models is fundamental to enhancing their utility across diverse applications, particularly in autonomous systems and complex problem-solving.
This paradigm shift in language model optimization, focusing on internal self-distillation, could lead to more capable and reliable AI agents and systems by leveraging richer predictive signals.
- · AI developers
- · Generative AI platforms
- · Companies deploying AI for complex tasks
- · Platforms reliant on less sophisticated AI
- · Traditional RL optimization methods
Language models become more efficient and perform better on reasoning tasks.
Accelerated development of more autonomous and reliable AI agents capable of handling intricate workflows.
Increased societal reliance on AI for decision-making in previously human-exclusive domains due to enhanced reasoning capacity.
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Read at arXiv cs.LG