Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

• URL: http://arxiv.org/abs/2607.07508v1
• Date: Wed, 08 Jul 2026 15:02:19 GMT
• Title: Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
• Abstract summary: We present Single-rollout Asynchronous Optimization (SAO) to address the stability and off-policy challenges in asynchronous RL.<n>SAO replaces group-wise sampling with single-rollout sampling, that is, using one rollout per prompt.<n>SAO is successfully deployed in the agentic RL pipeline for training the open GLM-5.2 model.
• Score: 47.00572734047258
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

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