arXiv:2603.07079v2 Announce Type: replace Abstract: On-policy distillation is a promising approach for transferring knowledge between language models, where a student learns from dense token-level signals along its own trajectories. This framework typically uses reverse KL divergence, encouraging the student to match the teacher's high-confidence predictions. However, we show that the mode-seeking property of reverse KL reduces generation diversity and yields unstable learning signals when the teacher distribution has high entropy. To address this, we introduce Entropy-Aware On-Policy Distilla
The continuous drive to improve the efficiency, diversity, and stability of large language models is leading to refinements in fundamental training techniques like distillation.
This development addresses key limitations in knowledge transfer between language models, which is crucial for creating more robust, diverse, and steerable AI systems.
The proposed 'Entropy-Aware On-Policy Distillation' method offers a way to train student language models that retain diversity and stability, overcoming existing challenges with traditional reverse KL divergence.
- · AI model developers
- · Companies using distilled language models
- · Researchers in machine learning
- · Users of AI-generated content
- · Teams struggling with model diversity and stability in distillation
- · Inefficient language model training techniques
Improved efficiency and performance of smaller student language models, especially in high-entropy contexts.
Faster deployment of specialized language models with higher quality, reducing computational costs for specific AI applications.
Enhanced ability to fine-tune and customize AI agents without sacrificing generative diversity or introducing instability, accelerating agentic AI development.
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Read at arXiv cs.LG