arXiv:2606.28562v1 Announce Type: new Abstract: On-policy distillation (OPD) has a property absent in offline distillation and RL: teacher supervision quality depends on student competence. Incoherent rollouts yield noisy gradients; already-mastered tokens yield redundant ones. This creates waste at three scales (tokens, training phases, and prompts) yet existing methods supervise uniformly. We introduce SEAD, which uses entropy as a unified probe of this competence-dependent degradation at three scales: (1) joint teacher-student entropy partitions tokens into zones receiving tailored divergen
The paper provides a new architecture for competence-aware on-policy distillation, addressing current challenges in AI agent training efficiency and effectiveness.
Improving the efficiency of on-policy distillation can significantly accelerate the development and deployment of more capable AI models, reducing compute waste and training time.
Existing uniform supervision methods in on-policy distillation may be replaced by more adaptive, entropy-guided approaches that tailor supervision based on student competence.
- · AI model developers
- · Cloud compute providers
- · AI research institutions
- · Generative AI companies
- · Developers relying on inefficient training methods
- · Companies with high compute costs for AI training
More efficient AI training workflows for large language models and other agentic systems become possible.
Reduced operational costs for AI development and deployment could broaden access to advanced AI capabilities.
The development of highly capable and cost-effective AI agents could accelerate, leading to novel applications across various industries.
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Read at arXiv cs.CL