MiMo V2.6 Pro RL

MiMo-V2.6-Pro-RL is the flagship checkpoint of Xiaomi's MiMo-V2.6 series, built around a single idea: scale reinforcement learning toward self-improvement. Rather than training separate specialists per domain, the model is trained in one mixed RL run spanning coding, general agent work, visual tasks, and cybersecurity, so that capabilities reinforce one another and learned strategies transfer to harnesses the model never saw in training.

It is natively omnimodal. Text, image, video, and audio are handled in one model, with a context window of 1M tokens intended for long repositories, extended tool traces, and multi-session agent runs.

Key Features

Native omnimodal input. Text, image, video, and audio are first-class inputs, handled by the same model.

One million token context. Long enough to hold entire repositories, long tool-call histories, and agent sessions that span many turns.

You only RL once. A single mixed reinforcement learning run covers coding, general agents, visual reasoning, and cybersecurity. Tasks and multiple harnesses are mixed into the same batch, which is what allows strategies to transfer across domains.

Scaled RL compute. Fully asynchronous Group Relative Policy Optimization over very large batches, at 1,568 prompts by 16 rollouts per step and billions of tokens per update.

Groupwise agentic grading. Binary pass or fail cannot rank solutions that all pass, so the reward signal itself is scaled. Groupwise Reward Synthesis builds task-specific rubrics offline from contrasting rollouts and fuses rubric quality with test outcomes. Groupwise Advantage Redistribution ranks passing trajectories online and shifts advantage toward higher-quality solutions. Because the model is judged against its own samples, this closes a self-improvement loop that favors shorter paths and fewer tokens per task.

Aligned RL. Training cold-starts from self-correction, where the model reflects on and rewrites its own misaligned turns into grounded next steps. Environment hardening, adversarial screening, and verifier cross-checks run throughout to keep the loop honest against reward hacking.

Multi-prefix multi-teacher on-policy distillation. After mixed RL, MOPD2 combines autonomous student rollouts with prefix-conditioned single-turn rollouts, reusing histories from teacher trajectories and demonstrations so that decision points can be trained without regenerating everything that preceded them. This extends the model into tasks that are hard to verify automatically.

Reasoning and tool calling. The model emits separate reasoning and answer channels and produces structured tool calls, with reasoning switchable per request.

Architecture

MiMo-V2.6-Pro-RL is a sparse Mixture-of-Experts model with 1.02 trillion total parameters, of which 42 billion are active on any given token. The backbone is 70 layers deep. The first block uses global attention with a dense feed-forward network; the remaining blocks interleave local sliding-window attention and global attention, and all of them use sparse MoE feed-forward networks with no shared experts. Ten layers use global attention and sixty use a sliding window of 128 tokens, which is what keeps a one-million-token context tractable. There are 384 routed experts with 8 activated per token.

Vision is handled by MiMo ViT, a 681M-parameter encoder of 28 layers, 24 sliding-window and 4 global, operating on 2 by 16 by 16 patches with 2 by 2 spatial merging. Audio runs through a 308M-parameter AudioTokenizer encoder using 20 residual vector-quantization codebooks, paired with a 127M-parameter audio patch encoder that groups four frames per patch.

The checkpoint also ships a speculative decoder: a five-layer sliding-window multi-token-prediction drafter that proposes seven subsequent tokens per forward pass for parallel verification.

Benchmarks

Xiaomi reports evaluation across four families of agentic task, and the headline result is the size of the jump over the previous generation rather than any single score.

On code agent work, MiMo-V2.6 Pro reaches 71.9 on DeepSWE v1.1 and 63.2 on MiMo Code Bench, against 19.0 and 40.4 respectively for MiMo-V2.5 Pro. On general agent benchmarks it scores 53.1 on AutomationBench v1.0.6, 76.9 on Toolathlon-Verified, 89.9 on Terminal Bench 2.1, 34.9 on the harder Terminal Bench 4.0, 82.0 on OSWorld-Verified, and 62.0 on JobBench, with an Agents' Last Exam score of 31.6. Cybersecurity shows the largest generational gap: 94.0 on CyberGym and 80.2 on MiMo Cyber Bench, where the previous generation scored 40.0 and 0.0. On visual agent work it records 72.3 on MiMo VisualCoding.

Use Cases

• Long-horizon coding agents working across whole repositories and extended tool traces
• General computer-use and automation agents that must operate unfamiliar harnesses
• Multimodal document, video, and audio understanding in a single pipeline
• Security research workflows, including vulnerability discovery and analysis
• Visual coding and UI reasoning from screenshots or recordings
• Multi-session assistants that need to carry very large contexts between turns

Model weights and the full technical report are published at XiaomiMiMo/MiMo-V2.6-Pro-RL. A smaller sibling in the same series is available as XiaomiMiMo/MiMo-V2.6-Flash-RL.