Xiaomi MiMo v2.6 ships: a deep dive into its RL scaling of batch size, environments, graders
tokenbender · x · 2026-09-22
Xiaomi released MiMo v2.6, and tokenbender published a detailed thread dissecting its RL-centric training approach. The paper's core idea is explicitly scaling three levers: batch size and throughput (argued primarily from a GPU-scaling and throughput perspective), environment diversity, and grader compute for reward evaluation. The thread walks through the rationale and tradeoffs behind each choice — a useful primer on current RL post-training scaling practice.
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