arXiv:2606.00305v1 Announce Type: new Abstract: On-Policy Distillation (OPD) improves large language model reasoning by training a student model on trajectories sampled from its own policy under teacher supervision. Although OPD operates on trajectories, its learning signal remains token-level: it identifies deviations through high-loss tokens and repairs them through local reverse-KL correction. We show that this "trajectory-sampled but token-learned" mechanism cannot reliably bridge student trajectories toward teacher trajectories. About 30% of high-loss tokens fall into the low-divergence r
This paper addresses a fundamental limitation in current on-policy distillation methods for large language models, suggesting a more robust approach to aligning student and teacher model reasoning trajectories.
Improved on-policy distillation techniques enable more efficient, performant, and potentially smaller AI models, impacting the cost and scalability of AI deployment and the development of specialized AI agents.
The proposed 'near-future guidance' mechanism moves beyond token-level corrections to directly bridge reasoning trajectories, offering a more effective pathway for large language models to learn complex reasoning from superior teachers.
- · AI researchers
- · Companies developing specialized LLMs
- · AI agent developers
- · Cloud providers offering AI services
- · Developers reliant solely on token-level fine-tuning
- · AI models with suboptimal reasoning capabilities
More capable and efficient AI models will become accessible as training processes improve.
This could accelerate the deployment of sophisticated AI agents across various sectors, automating complex tasks.
The reduced computational cost and improved performance of these models might lower the barrier to entry for AI development, fostering greater innovation and competition.
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Read at arXiv cs.CL