Asynchronous Single-Rollout RL for Agents
de4dee · reddit · 2026-07-14
The paper proposes Single-Rollout Asynchronous Optimization (SAO) to address the stability and off-policy issues of asynchronous RL in long-horizon agent tasks.
Main Approach
- Replaces the intra-group sampling common in GRPO with a single rollout to reduce off-policy deviation during asynchronous training
- Introduces a more practical value model training design
- Incorporates strict two-sided token-level clipping to improve optimization stability
Results
- Training stably runs for 1,000 steps and consistently outperforms GRPO and its variants on benchmarks like SWE-Bench Verified, BeyondAIME, and IMOAnswerBench
- Also performs better in simulated online learning scenarios, demonstrating adaptability to changing environments
- This method has already been applied to the agentic RL training pipeline for the open-source GLM-5.2
Related event: SAO Outperforms GRPO in Coding and Reasoning Benchmarks(3 posts)→
More from coding & agent
- Anthropic researcher: 99% of engineers now run swarms of 300+ self-improving agents — AlishaOutridge · 2026-09-11
- Gergely Orosz: Shipping 10x PRs With AI Agents, Sites Fill With Small Regressions — ducha_aiki · 2026-09-11
- Same Echo Maze prompt, three frontier models: all passed visually but shipped the same hidden bug — eyishazyer · 2026-09-11
- Astra storyboards plus Minimax H3 per-shot generation boost video success rates — Hailuo_AI · 2026-09-11
- Codex tip: use Sol with Astra and Luna sub-agents to save usage — pvncher · 2026-09-11
- agents-best-practices: a provider-neutral Agent Skill for designing and auditing agentic harnesses — tom_doerr · 2026-09-11