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PRIME-RL is a framework for large-scale asynchronous reinforcement learning, designed to be hackable and scale to 1000+ GPUs with support for various models and environments.
Discusses a new paper from Tsinghua/Zhipu AI on asynchronous reinforcement learning for agents, and notes that their previous GLM-5.2 model uses a critic instead of GRPO.
AsyncWebRL introduces an asynchronous multi-step reinforcement learning system for vision-language web agents, achieving up to 2.9x training speedup and setting a new state-of-the-art on WebGym by replacing per-trajectory normalization with a constant to reduce trajectory length inefficiency.