Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence
arXiv:2605.13230v2 Announce Type: replace Abstract: On-policy distillation (OPD) has become a promising paradigm for reasoning-oriented post-training of large language models (LLMs), especially when combined with reinforcement learning from verifiable rewards (RLVR). Existing OPD methods rely on reverse KL (RKL)-based teacher supervision over trajectories sampled from the student policy. However, we identify a critical limitation: under large teacher--student policy divergence, RL-driven exploration often produces trajectories outside the teacher distribution, resulting in uninformative negati
This research addresses a critical limitation in current on-policy distillation (OPD) methods for Large Language Models (LLMs) used with reinforcement learning, indicating active development in AI training methodologies.
Improved policy optimization methods for LLMs can lead to more robust and effective AI agents, accelerating their deployment and capabilities in complex reasoning tasks.
The ability to perform effective reasoning distillation even under large teacher-student policy divergence expands the potential applications and reliability of LLMs, especially in real-world, dynamic environments.
- · AI developers
- · LLM-powered automation platforms
- · Reinforcement learning researchers
More sophisticated and reliable AI agents become viable for deployment across various sectors.
This could lead to increased adoption of LLM-based solutions in critical applications, boosting productivity and potentially displacing some human tasks.
Enhanced AI reasoning capabilities might accelerate the development of more generalized and autonomous AI systems, leading to unforeseen societal and economic shifts.
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Read at arXiv cs.LG