Xiaomi MiMo v2.6 ships with RL as the hero, scaling batch size, env diversity and grader compute
tokenbender · x · 2026-09-22
tokenbender kicks off a deep-dive thread on Xiaomi MiMo v2.6, arguing RL is the real story of the release. The paper's core idea is explicitly scaling three things: batch size and throughput, environment diversity, and grader compute. He notes the large-batch choice is justified mainly by throughput and GPU scaling ease, and wishes the paper had discussed whether big batches help or hinder when training across orthogonal task types.
Related event: Xiaomi releases MiMo v2.6 with scaled RL training at its core(6 posts)→
More from Models
- AI crowd discovers LLMs aren't always the cheapest, most effective tool — evilsocket · 2026-09-22
- cloneofsimo: academia badly underestimates the problems OpenAI's math agents are solving — cloneofsimo · 2026-09-22
- Founder finds asking the model to compare outputs restores drifting Astra quality — i_dg23 · 2026-09-22
- Community wonders if Alibaba has abandoned its Qwen 35B A3B small MoE line — Akainu_Fan · 2026-09-22
- Claude Opus 'acting like Sonnet' fuels speculation of new model launch — RyanMorrisonJer · 2026-09-22
- Reddit proposes measuring LLMs by cost per accepted task, not cost per token, after Grok 4.7 launch — Crescitaly · 2026-09-22