Prime Intellect Releases prime-rl 0.6.0 to Train Trillion-Parameter MoE Models on Agentic RL Workloads
Prime Intellect has released prime-rl version 0.6.0. The framework targets reinforcement learning on trillion-parameter Mixture-of-Experts (MoE) models. It focuses on heavy agentic workloads, like long-horizon software-engineering tasks. The research team trained GLM-5 on SWE tasks at up to 131k sequence length. Step times stayed under five minutes. The batch size was 256 rollouts. The run used only 28 H200 nodes. TL;DR prime-rl 0.6.0 trains trillion-parameter MoE models on agentic RL
Prime Intellect released an open framework called prime-rl 0.6.0 that trains very large AI models on reinforcement learning tasks, specifically focusing on long-horizon software engineering work. The framework separates the training and inference systems to run independently, avoiding delays when some tasks take much longer than others to complete. Key technical innovations include using lower-precision math (FP8) for faster processing, spreading experts across many GPUs, separating prefill and decode operations, and managing large memory caches through tiering. The approach demonstrated the ability to train a trillion-parameter model on complex coding tasks with reasonable resource requirements.
Explainer: What Is a Mixture of Experts Model and Why Does It Use Fewer Resources?
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