Author

Shuai Zhang

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#artificial intelligence May 2026

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

AOPD replaces ineffective negative reinforcement with localized divergence minimization in non-positive advantage regions while preserving positive reinforcement learning and maintains higher policy entropy during training and better capability retention during sequential tool-use adaptation.

Nan Jia, Haojin Yang, Xing-Chen Ma et al. · arXiv.org · 18 citations · ⚡5

Jul 2026

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills and distills their behavioral effect back into the policy model, is proposed.

Jinyang Wu, Shuo Yang, Zhengxi Lu et al. · arXiv.org · 7 citations