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

Source: arXiv cs.LG — read the full report at the original publisher.

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