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Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning

31 upvotes · 21 JUL 2026 · Junyao Yang, Yucheng Shi, Zongxia Li et al.

This paper develops a new method to improve the stability of asynchronous reinforcement learning by adapting the trust region to account for staleness, which is a common problem in this field. Practitioners might care about this because stable reinforcement learning can lead to better performance and more efficient training.

trust regionsasynchronous reinforcement learningstalenesspolicy lagrollout generationoptimization