Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level

Abstract:On-policy distillation (OPD) trains a student on its own trajectories with token-level teacher feedback and often outperforms off-policy distillation and standard reinforcement learning. However, we find that its standard advantage weighted policy gradient suffers from three structural weaknesses, including high variance updates, vanishing gradients in zero-advantage regions, and exploration bottlenecks when corrective signals are insufficient. We therefore propose Asymmetric On-Policy Distillation (AOPD), which replaces ineffective negative reinforcement with localized divergence minimization in non-positive advantage regions while preserving positive reinforcement learning. Experiments on mathematical reasoning benchmarks show that AOPD consistently outperforms standard OPD, with average gains of 4.09 / 8.34 under strong / weak initialization, respectively. AOPD also maintains higher policy entropy during training and better capability retention during sequential tool-use adaptation.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2605.06387 [cs.LG]

(or arXiv:2605.06387v4 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.06387 Focus to learn more arXiv-issued DOI via DataCite

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