Xiaomi's MiMo-V2.6 Bets on Reinforcement Learning Scaling Across 1000+ TPUs
Xiaomi released MiMo-V2.6, its largest reinforcement learning scaling effort to date, spanning coding, general reasoning, vision, automation and cybersecurity. The team completed full-parameter RL training on the 310B model across 1000+ TPUs, stably running 1000+ steps and processing 2.7-3.7 billion tokens per step. Luo Fuli's retrospective frames the core question: after years of pretraining scaling laws, can RL scale capability the same way—Xiaomi has been betting on this for the past half year.
Confirmed
- The paper explicitly scales three RL factors: batch size and throughput, environment diversity, and grader compute. Per @DrSingularity, each RL step processes about 1568 samples and 2.7-3.7 billion tokens, with context lengths up to 1 million tokens.
- The MiMo team disclosed JAX+TPU-based large-scale RL: full-parameter RL training of the 310B MiMo-V2.6 on 1000+ TPUs, stably running 1000+ steps; scaling up mainly requires config changes rather than code rewrites.
- @tokenbender's thread notes the large-batch justification stays at throughput and GPU-scaling convenience. Environment synthesis follows standard good practices—clear specs, sampling from rich real-world scenario pools, curating quality seed tasks with grounding. Section 4.2.5's multi-harness training earned his approval, as open-source users often build their own harnesses.
- The paper discusses CodeMidas's source-driven synthesis. Luo Fuli's retrospective mentioned RL scale details such as generating 25,000 agent trajectories per step.
Unconfirmed
- Long-horizon tasks appear to rely mainly on agent-driven synthesis; tokenbender is skeptical of their effectiveness and plans to follow up.
Why it matters
- tokenbender observes MiMo's tasks are grounded from diverse sources rather than built from scratch—pure self-play is not a substitute; since the tasks resemble daily workflows, he expects MiMo v2.6 could replace Sol for daily work.
- Running 310B full-parameter RL on JAX+TPU with config-only scaling offers the open-source community an engineering reference for large-scale RL training.
2026-09-22 ~ 2026-09-23 · 10 related posts
- Episode 1: Xiaomi MiMo RL Training Details Spark Debate: Suspected 1M-Context RL, Big Gains in Just 30 Steps(2026-09-22, 15 posts)
- Episode 2: Xiaomi's MiMo-V2.6 Bets on Reinforcement Learning Scaling Across 1000+ TPUs(2026-09-22, 10 posts)
- Episode 3: Xiaomi's MoE RL stability paper: router-aware GRPO tweaks explained(2026-09-22, 2 posts)
Primary sources
- Xiaomi MiMo-V2.6 scales RL to 3.7B tokens per step, targeting self-improvement — Dr_Singularity ·
- Xiaomi MiMo v2.6 ships with RL as the hero, scaling batch size, env diversity and grader compute — tokenbender ·
- Xiaomi's Luo Fuli Recaps MiMo-V2.6 RL Run: 25K Agent Trajectories Per Step — bookwormengr ·
- Full-Parameter RL on TPUs: peano_ai Runs 310B MiMo-V2.6 Across 1,000+ TPUs — simonguozirui · 2026-09-22
- Xiaomi MiMo runs full-parameter RL on 310B model across 1000+ TPUs with JAX — AccBalanced · 2026-09-22
- [source] Xiaomi MiMo v2.6 ships with RL as the hero, scaling batch size, env diversity and grader compute — tokenbender · 2026-09-22
- Thread recap: MiMo v2.6's environment scaling principles grounded in diverse real sources — tokenbender · 2026-09-22
- MiMo leans on CodeMidas-style source-driven synthesis and agent-driven long-horizon tasks — tokenbender · 2026-09-22
- MiMo v2.6 details: grounded environment synthesis and multi-harness training for open source users — tokenbender · 2026-09-22
- Dev breaks down paper: multi-harness training and environment synthesis principles — tokenbender · 2026-09-22
- [source] Xiaomi's Luo Fuli Recaps MiMo-V2.6 RL Run: 25K Agent Trajectories Per Step — bookwormengr · 2026-09-22
- [source] Xiaomi MiMo-V2.6 scales RL to 3.7B tokens per step, targeting self-improvement — Dr_Singularity · 2026-09-23
1 near-duplicate retellings: tokenbender