arXiv:2606.29340v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) trains a reasoning model on rollouts sampled from its own policy by matching a privileged teacher that also sees verified reference solutions. Existing OPSD objectives supervise only the output distribution, so privileged context affects training through a token-level divergence without directly supervising the internal computation that produced that distribution. We propose Privileged Hidden Flow (PHF), which additionally distills how a privileged teacher's hidden states move along the same rollout. Rather than
The continuous drive for more efficient and robust AI training methods, especially in the context of complex reasoning tasks, necessitates advancements in self-distillation techniques.
This breakthrough could lead to more performant and robust AI models that learn highly complex reasoning processes more effectively, reducing reliance on massive datasets for every new task.
AI training paradigms are shifting towards more nuanced self-supervision, moving beyond just output-level matching to internal computational process matching, which allows for richer knowledge transfer.
- · AI model developers
- · Companies deploying complex reasoning AI
- · Research institutions in machine learning
- · brute-force compute-heavy AI training methods
AI models will achieve higher reasoning capabilities with less human supervision.
The cost of developing highly capable AI agents could decrease, democratizing advanced AI.
More sophisticated autonomous AI agents could emerge, capable of tackling more complex real-world problems.
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Read at arXiv cs.AI