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Li$^{2}$SAO: Lifting Light Singl-Rollout Asynchronous RL Optimization via Group-Relative Actor-Critic Bootstrapping
J. D. Coder, Author 2, Author 3, Author 4, Author 5
reinforcement learningactor-criticasynchronous optimization
2610.40726
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1h ago80%
Abstract
This paper presents Li$^{2}$SAO, an improvement over Single-rollout Asynchronous Optimization that enhances early training performance in reinforcement learning tasks.
Reality Card
Core Claim
Li$^{2}$SAO improves early training performance and can surpass GRPO in later stages, addressing the limitations of previous models.
Method / Result
Li$^{2}$SAO catches up with or surpasses GRPO in early training and raises the later performance ceiling.
Limitations
Li$^{2}$SAO relies on a value model that is not yet well trained, which may affect early performance.
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