arXiv:2604.14084v3 Announce Type: replace Abstract: On-policy knowledge distillation (OPD) trains a student on its own rollouts under token-level supervision from a teacher. Not all token positions matter equally, but existing views of token importance are incomplete. We ask a direct question: which tokens carry the most useful learning signal in OPD? Our answer is that informative tokens come from two regions: positions with high student entropy, and positions with low student entropy plus high teacher--student divergence, where the student is overconfident and wrong. Empirically, student ent
This research addresses a fundamental challenge in on-policy knowledge distillation, a critical technique for improving efficiency and performance in AI models, particularly in agentic systems.
Improved token importance understanding in knowledge distillation can lead to more efficient and capable AI agents, impacting their development and deployment across various sectors.
The proposed method offers a refined approach to identifying instructional tokens, allowing for more targeted and effective learning in student models during on-policy distillation.
- · AI model developers
- · Organizations deploying AI agents
- · Researchers in machine learning efficiency
- · Inefficient AI training methods
- · Models reliant on naive distillation techniques
AI agents become more performant and energy-efficient due to optimized training.
Faster development cycles for complex AI systems as model training becomes more effective.
Broader adoption of AI agents in critical applications due to increased reliability and reduced computational overhead.
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