Train 100B Parameter Agent Model on 6x H200s in 2 Days
willccbb · x · 2026-07-13
Developer @willccbb shared an impressive infrastructure and algorithm co-design achievement: it now takes only 6 H200 nodes to complete 1000 steps of reinforcement learning (RL) training in 2 days.
- Training target: A 100B parameter reasoning model.
- Task scenario: SWE (Software Engineering) agentic tasks with up to 40 rounds of conversation.
- Core advantage: Allows developers to train using their own programming frameworks.
The cited tweet points out that adapting the "model-framework-task" in the weight space has become incredibly easy. Developers can focus on domain-specific evaluations and data, yielding highly efficient task-specific agents at extremely low compute costs.
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