Asynchronous Reinforcement Learning Unsuitable for GRPO
ziv_ravid · x · 2026-07-11
This post summarizes a new paper from Tsinghua / Z.AI focusing on asynchronous reinforcement learning for agents.
The key takeaways are:
- For GRPO, asynchronous training is largely unsuitable because a group must wait for the slowest rollout to finish.
- When agent tasks involve trajectory lengths reaching the 128k token mark, this waiting period becomes excessively long, causing data to become "stale" before training.
- The post also mentions that the release notes for GLM-5.2 indicated they used a critic rather than GRPO.
Related event: GLM Team Proposes SAO Algorithm for Asynchronous Agent RL(15 posts)→
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