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@DirhousssiAmine

šŸŒ² Tree packing has landed in AsyncGRPO in TRL! āš” 26Ɨ less trainer compute to reach the same reward on agentic RL. GRPO starts from the same prompt G times, and forked multi-turn rollouts share most of their history. Sequence packing forwards those shared tokens once per sample. Tree packing forwards each unique token once. The first version used FlexAttention, but the shipped one is a single FA3 varlen call. Each token retains its own target, advantage, and previous log-prob. Extensively tested against sequence packing on dense, sliding-window, softcap, MoE, and VLM models. When it can't pack anything, it costs ~1%. On a REAL agentic RL run on MiMo Envs, tree packing reached the same reward with 26Ɨ less trainer compute šŸ‘‡

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