[2607.07508] Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

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arXiv:2607.07508 (cs)

[Submitted on 8 Jul 2026]

Title:Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

Authors:Zhenyu Hou, Yujiang Li, Jie Tang, Yuxiao Dong<br>View a PDF of the paper titled Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning, by Zhenyu Hou and 3 other authors

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Abstract:Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-interleaved, which is inefficient for long-horizon agentic tasks. Recently, asynchronous RL has emerged as a more efficient alternative by updating the model as rollouts arrive. However, existing asynchronous RL systems often emphasize throughput, while leaving training stability and task effectiveness largely underexplored. For example, a key challenge is that group-wise sampling in the widely-used GRPO framework does not naturally fit asynchronous agentic training. In this paper, we present Single-rollout Asynchronous Optimization (SAO) to address the stability and off-policy challenges in asynchronous RL. To reduce off-policy effects and improve generalization, we replace group-wise sampling with single-rollout sampling, that is, using one rollout per prompt. We further improve this single-rollout strategy with practical value-model training designs. To improve optimization stability, we introduce a strict double-side token-level clipping strategy. SAO is able to train stably for one thousand steps and consistently outperform GRPO and its variants on agentic coding and reasoning benchmarks, such as SWE-Bench Verified, BeyondAIME, and IMOAnswerBench. We also demonstrate that single-rollout RL is particularly effective in a simulated online learning setting, where the model must adapt to changing evolving environments. To this end, SAO is successfully deployed in the agentic RL pipeline for training the open GLM-5.2 model (750B-A40B).

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2607.07508 [cs.LG]

(or<br>arXiv:2607.07508v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.07508

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arXiv-issued DOI via DataCite

Submission history<br>From: Zhenyu Hou [view email]<br>[v1]<br>Wed, 8 Jul 2026 15:02:19 UTC (341 KB)

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