Xenova releases an in-browser playground for steering two neurons in Qwen3.5-4B

Wing Lian (caseus)@winglian
You can now use AsyncGRPO with tree packing on your favorite hybrid models such as @Alibaba_Qwen 3.5 (GDN), @liquidai LFM 2.5, and @NVIDIAAI Nemotron 3.5 (Mamba2). Built as a standalone component that hooks into @huggingface Transformers and leverages Hub kernels. x.com/DirhousssiAmin…
Dirhousssi Amine@DirhousssiAmine
🌲 Tree packing has landed in AsyncGRPO in TRL! ⚡ 26× 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 26× less trainer compute 👇
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