arXiv:2606.11709v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) provides dense, token-level supervision for reasoning models by aligning a model's own distribution with the distribution it produces under privileged context, typically a verified solution. However, we show that the learning signal drawn from this distributional gap concentrates on style tokens rather than task-bearing ones, as the hinted model tends to produce more direct, shorter outputs. We term this pathology \emph{privilege-induced style drift}, which destabilizes training or causes response length to sh
This research addresses a specific challenge in current reinforcement learning techniques for reasoning models, indicating ongoing advancements and refinements in AI training methodologies.
Improving self-distillation methods can lead to more efficient and effective training of AI models, enhancing their reasoning capabilities and reducing computational overhead.
The proposed RLCSD method aims to overcome limitations in on-policy self-distillation by focusing learning signals on task-bearing tokens, potentially leading to more robust and less 'stylized' AI outputs.
- · AI researchers
- · Developers of reasoning models
- · Companies investing in advanced AI
- · AI models prone to 'privilege-induced style drift'
- · Methods that produce lengthy or indirect AI outputs
More accurate and concise AI outputs for reasoning tasks due to better learning signal focus.
Reduced computational costs and accelerated development cycles for complex AI systems.
Enhanced AI agent capabilities, leading to more reliable autonomous systems in various applications.
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Read at arXiv cs.CL