A Survey of On-Policy Distillation for Large Language Models

arXiv:2604.00626v5 Announce Type: replace Abstract: On-Policy Distillation (OPD) trains large language models with teacher guidance on student-generated trajectories, addressing the mismatch between fixed demonstrations and the histories encountered during deployment. As its use expands across language model training, different formulations of supervision and optimization make it difficult to compare methods and understand when they are effective. This survey develops a unified view of OPD through the interplay between student-generated states, teacher information, and policy updates. This framework connects the foundations of distillation with self-distillation, multi-teacher integration, reinforcement learning, and interactive agents. We examine how these settings change the supervision available to the student and relate the resulting design choices to capability transfer, training efficiency, and deployment. Our synthesis centers on the conditions under which guidance remains reliable and learnable as the student evolves, considering both immediate performance and the capacity for subsequent learning. We conclude by discussing open challenges in adapting supervision and allocating computation to support effective learning.