@samsja19: prime-rl can now train 1T parameters MoE blazingly fast, under 5 minutes per step, or 1k steps in ~3 days To achieve th…

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Summary

Prime Intellect released prime-rl v0.6.0, enabling reinforcement learning at trillion-parameter MoE scale with sub-5-minute step times and optimized inference, training, and rollout.

prime-rl can now train 1T parameters MoE blazingly fast, under 5 minutes per step, or 1k steps in ~3 days To achieve this we shipped in our latest prime-rl 0.6.0: * inference: wide-ep, fp8 inference, llm-d router, mooncake, kv cache cpu offloading * training: fsdp2, deep-ep expert parallelism, dsa cp, fp8 training, router replay * agentic rollout: we rewrote the core of our rollout orchestrator for better scalability support for glm5, kimi, nemotron, ..., prime-rl is open source but also end to end optimized to run on our dedicated RL infra and compute layer
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Cached at: 06/23/26, 03:52 PM

prime-rl can now train 1T parameters MoE blazingly fast, under 5 minutes per step, or 1k steps in ~3 days

To achieve this we shipped in our latest prime-rl 0.6.0:

  • inference: wide-ep, fp8 inference, llm-d router, mooncake, kv cache cpu offloading

  • training: fsdp2, deep-ep expert parallelism, dsa cp, fp8 training, router replay

  • agentic rollout: we rewrote the core of our rollout orchestrator for better scalability

support for glm5, kimi, nemotron, …,

prime-rl is open source but also end to end optimized to run on our dedicated RL infra and compute layer

Prime Intellect (@PrimeIntellect): Today we’re releasing prime-rl v0.6.0 — enabling RL at trillion-parameter MoE scale on agentic workloads at the highest efficiency.

We’ve relentlessly optimized our RL infra.

The result: GLM-5 on agentic SWE tasks at 131k context and sub-5-minute step time.

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