arXiv:2606.01249v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent learning, multi-task enhancement, and model compression. However, OPD training becomes unstable when the teacher and student distributions differ substantially, as teacher supervision on student-generated tokens may yield unreliable policy gradients and even cause optimization failure. This work addresses reliable on-policy token-level supervision through credit assignment strategies, and proposes
The paper addresses a core issue in efficient LLM post-training, which is critical as LLM deployment and application scale rapidly across various domains.
Improved stability and reliability in on-policy distillation enables more robust, efficient, and compressible LLMs, accelerating their integration into real-world applications and agentic systems.
The stability of on-policy distillation for LLMs is enhanced, allowing for more reliable and efficient fine-tuning and compression without optimization failures due to distribution mismatches.
- · AI developers
- · Cloud providers
- · Large Language Model (LLM) platforms
- · AI research institutions
- · Less efficient LLM training methods
More efficient and stable LLM post-training reduces computational costs and accelerates model deployment.
This efficiency allows for more complex and specialized LLM applications, potentially fostering new AI agent capabilities.
The widespread deployment of robust, efficient LLMs could further decentralize sophisticated AI capabilities, making them accessible to a broader range of developers and enterprises.
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Read at arXiv cs.CL