REINFORCE-Zero: kernel alignment + unclipped IS enables 20K-step stable async RL

青稞AI · wechat · 2026-11-06

The REINFORCE-Zero project reports a first for the open-source community: by aligning trainer and inference numerical execution (Trainer–Inference Invariance, TII), plain unclipped token-level importance sampling suffices for stable asynchronous LLM RL for 20,000 policy updates — no ratio clipping, no rejection gates.

Core insight

Results

The kernel-alignment workflow is packaged as a reusable agent skill (11 checks, C0-C10) used with Claude Code to wire vLLM's batch-invariant kernels into AutoModel. Paper, code, and skill are all open-sourced.

Related event: REINFORCE Zero: Open-Source Asynchronous LLM RL Runs Stably for 20K Steps(3 posts)→

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