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WarpSAC: Towards the Pinnacle of Scalable Off-policy RL by Rethinking Exploration and Exploitation

135 upvotes · 25 AUG 2026 · Zihao Wu, Hongyao Tang, Yi Ma et al.

This paper proposes a new approach to off-policy reinforcement learning (RL) that adapts to different data regimes, allowing for more efficient training on large datasets. Practitioners might care about this paper because it offers a scalable solution for RL tasks that can handle varying levels of data availability.

off-policy RLexploration-exploitation tradeoffstabilizersregime-awareparallel simulationreinforcement learning benchmarks