Preprint
Aug 2026
Agent-G$^2$: Gaussian Guidance for Agentic Reinforcement Learning
A Gaussian guidance framework that draws the depth per task from a Gaussian whose center and spread are estimated online from rollouts already collected for policy optimization, requiring no probe rollouts or learned depth predictor is proposed.
Zi-Xuan Wang, Yan-Rui Miao, Zhengxi Lu et al.
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