Xiaomi releases MiMo v2.6 with scaled RL training at its core

Xiaomi released MiMo v2.6, and @tokenbender quickly followed with an in-depth long thread, arguing that the real star of this release is reinforcement learning rather than any flashy components. The paper's core idea is to explicitly scale three variables: batch size and throughput, environment diversity, and grader compute. The author notes that the official justification for large batches rests mainly on throughput and GPU-scaling convenience.

Confirmed

• The MiMo v2.6 paper advocates large batches from the perspective of throughput and GPU-scaling convenience, explicitly scaling the three RL levers: batch size and throughput, environment diversity, and grader compute.
• Section 4.2.5 trains with multiple harnesses; tokenbender considers this commendable, since open-source users each have their own preferences and often build harnesses suited to their own tasks.
• Environment synthesis follows the generally good principles of data synthesis: a clear spec, sampling from a rich pool of real-world scenarios, and curating high-quality seed tasks with grounding. The author's verdict: "nothing fancy, but these principles are non-negotiable."
• The paper discusses CodeMidas's source-code-driven synthesis approach.

Unconfirmed

• The long-horizon task portion seems to be done mainly via agent-driven synthesis; tokenbender doubts its effectiveness and says he is interested in following up to verify.

Why it matters

• tokenbender points out that MiMo's tasks are not built from scratch but grounded from diverse sources, which pure self-play cannot replace; the tasks are close to everyday workflows, so the author is hopeful that MiMo v2.6 could replace Sol for daily work.

2026-09-22 ~ 2026-09-22 · 6 related posts

• [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
• [source] 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

1 near-duplicate retellings: tokenbender