--- license: mit language: - en - zh tags: - text-generation - multimodal - vision-language - audio - agent - video-understanding - long-context - mimo_v2 - transformers library_name: transformers ---

Xiaomi-MiMo

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# MiMo-V2.6-Flash-RL **Scaling Reinforcement Learning Toward Self-Improvement**

Technical Report

## 1. Introduction MiMo-V2.6-Flash-RL is the efficiency-balanced checkpoint of the MiMo-V2.6 series. The series is built to **scale reinforcement learning toward self-improvement** β€” scaling RL compute, environment diversity, and grader compute together, so the model keeps expanding its capability frontier through exploration and feedback. Key features include: - **Native Omnimodal + Long Horizon**: Text, image, video, and audio in one model; 1M tokens for long repositories, tool traces, and multi-session agent runs. - **You Only RL Once**: One mixed RL run across coding, general agents, visual, and cybersecurity β€” not separate per-domain runs. Tasks and multiple harnesses are mixed in the same batch so capabilities reinforce each other and strategies transfer to harnesses never seen in training. - **Scaling RL Compute**: Fully asynchronous Group Relative Policy Optimization (GRPO) on very large batches β€” 1,568 prompts Γ— 16 rollouts per step, billions of tokens per update. - **Groupwise Agentic Grading (Self-Improvement Loop)**: Binary pass/fail cannot rank passing solutions, so the reward signal itself is scaled. An agentic grader compares rollouts *within each group*: **Groupwise Reward Synthesis (GRS)** builds task-specific rubrics offline from contrasting rollouts and fuses rubric quality with test outcomes; **Groupwise Advantage Redistribution (GAR)** ranks passing trajectories online and moves advantage toward higher-quality solutions. Judged against the policy’s own samples, this closes a self-improvement loop and steers toward shorter paths and fewer tokens per task. - **Aligned RL**: Cold start from self-correction β€” the model reflects on and rewrites its own misaligned turns into grounded next steps. Throughout RL, environment hardening, adversarial screening, and verifier cross-checks keep the loop honest against reward hacking. - **Multi-Prefix Multi-Teacher On-Policy Distillation (MOPD2)**: After mixed RL, MOPD2 combines autonomous student rollouts with prefix-conditioned single-turn rollouts (Teacher-Prefix and SFT-Prefix), reusing histories from teacher trajectories and SFT demonstrations so decision points train without regenerating preceding turns β€” extending capabilities to hard-to-verify tasks. ## Model Summary - **Architecture**: Sparse MoE (Mixture of Experts), 309B total / 15B activated parameters - **Context Length**: 1M tokens - **Modalities**: Text, Image, Video, Audio - **Vision Encoder**: 681M-param MiMo ViT (28 layers: 24 SWA + 4 Full) - **Audio Encoder**: 308M AudioTokenizer + 127M audio patch encoder - **Multi-Token Prediction (MTP)**: 5-layer speculative decoder ![Figure 1: MiMo-V2.6 architecture β€” omni encoders, hybrid SWA backbone, and MTP blocks](assets/architecture.png) *Figure 1. MiMo-V2.6 architecture.* ## 2. Downloads | Model | Download | | --- | --- | | **MiMo-V2.6-Pro-RL** | [πŸ€— HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) Β· [πŸ€– ModelScope](https://www.modelscope.cn/models/XiaomiMiMo/MiMo-V2.6-Pro-RL) | | **MiMo-V2.6-Flash-RL** | [πŸ€— HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL) Β· [πŸ€– ModelScope](https://www.modelscope.cn/models/XiaomiMiMo/MiMo-V2.6-Flash-RL) | ## 3. Evaluation Results | Benchmark | MiMo-V2.6 Pro | MiMo-V2.6 Flash | MiMo-V2.5 Pro | Claude Opus 5 | GPT-5.6 Sol | Claude Fable 5 | | --- | --- | --- | --- | --- | --- | --- | | **Code Agent** | | | | | | | | DeepSWE v1.1 | 71.9 | 67.9 | 19.0 | 74.0 | 73.0 | 70.0 | | ProgramBench | 26.5 | 26.0 | 12.5 | 37.0 | 25.0 | 33.0 | | MiMo Code Bench | 63.2 | 61.2 | 40.4 | 68.6 | 59.3 | - | | **General Agent** | | | | | | | | AutomationBench v1.0.6 | 53.1 | 52.3 | 16.0 | 50.3 | 45.8 | 46.2 | | Toolathlon-Verified | 76.9 | 73.6 | 49.1 | 80.6 | 74.9 | 77.9 | | GDPval-AA 2.1 | 1673 | - | 1107 | 1708 | 1588 | 1595 | | Agents’ Last Exam | 31.6 | 27.6 | 13.2 | 31.6 | 30.8 | 25.7 | | Terminal Bench 4.0 | 34.9 | 28.8 | 1.5 | 49.0 | 39.9 | 42.4 | | Terminal Bench 2.1 | 89.9 | 87.6 | 65.2 | 89.1 | 88.8 | 84.3 | | OSWorld-Verified | 82.0 | 80.8 | - | 83.4 | 83.0 | 86.0 | | JobBench | 62.0 | 61.2 | 25.0 | 65.7 | 45.4 | 57.4 | | **Cybersecurity** | | | | | | | | CyberGym | 94.0 | 95.1 | 40.0 | - | - | - | | MiMo Cyber Bench | 80.2 | 77.2 | 0.0 | - | - | - | | ExploitGym | 17.8 | 6.0 | 0.2 | 22.1 | 30.3 | 28.4 | | ExploitBench | 47.9 | 25.3 | 16.6 | 70.0 | 78.5 | 78.0 | | SEC Bench Pro | 66.3 | 47.5 | 17.7 | - | 79.1 | - | | **Visual Agent** | | | | | | | | MiMo VisualCoding | 72.3 | 71.5 | - | 70.0 | 73.4 | 69.1 | ## 4. Model Architecture ### LLM Backbone | Component | MiMo-V2.6-Flash-RL | | --- | --- | | Layers (Total / SWA / GA) | 48 / 39 / 9 | | Hidden Size | 4096 | | SWA Heads (Q/KV) | 64 / 8 | | GA Heads (Q/KV) | 64 / 4 | | Head Dimensions (QK / V) | 192 / 128 | | Sliding Window Size | 128 | | Routed Experts (Total / Activated) | 256 / 8 | | Max Context Length | 1M | | MTP / Speculative Decoder | 5 SWA layers, window 1024 | The first Transformer block uses global attention with a dense FFN. Remaining blocks interleave local SWA and GA; both use sparse MoE FFNs without shared experts. ### Vision Encoder (MiMo ViT) | Configuration | Value | | --- | --- | | Layers (Total / SWA / GA) | 28 / 24 / 4 | | Hidden Size | 1280 | | Attention Heads (Q / KV) | 32 / 8 | | Head Dimension | 64 | | Patch Size (T Γ— H Γ— W) | 2 Γ— 16 Γ— 16 | | Sliding Window (Left / Right) | 64 / 64 | | Spatial Merge Size | 2 Γ— 2 | | Parameters | 681M | ### Audio Encoders AudioTokenizer encoder: 24 layers (12 SWA / 12 GA), hidden 1024, 20 RVQ codebooks, 308M parameters. Audio patch encoder: 6 layers, 127M parameters; four frames per patch (25 Hz β†’ 6.25 Hz). ### Speculative Decoder 5-layer SWA MTP drafter (DFlash-style). Predicts 7 subsequent tokens per forward pass for parallel verification. ## 5. Deployment For best performance, follow the [SGLang MiMo cookbook](https://docs.sglang.io/cookbook/autoregressive/Xiaomi/MiMo-V2.5). Docker image: `lmsysorg/sglang:latest`. ### SGLang ```bash sglang serve \ --trust-remote-code \ --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL \ --tp 8 \ --dp 2 \ --enable-dp-attention \ --enable-dp-lm-head \ --mm-enable-dp-encoder \ --mem-fraction-static 0.65 \ --chunked-prefill-size 16384 \ --speculative-algorithm EAGLE \ --speculative-num-steps 3 \ --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 4 \ --enable-multi-layer-eagle \ --reasoning-parser mimo \ --tool-call-parser mimo \ --host 0.0.0.0 \ --port 30000 ``` ### vLLM Follow the [vLLM MiMo-V2.5 recipe](https://recipes.vllm.ai/XiaomiMiMo/MiMo-V2.5). Stable vLLM may lag; pre-built image: `docker pull vllm/vllm-openai:mimov25-cu129`. ```bash vllm serve XiaomiMiMo/MiMo-V2.6-Flash-RL \ --tensor-parallel-size 4 \ --trust-remote-code \ --gpu-memory-utilization 0.95 \ --max-model-len auto \ --reasoning-parser mimo \ --tool-call-parser mimo \ --enable-auto-tool-choice \ --generation-config vllm ``` Recommended sampling: `temperature=1.0`, `top_p=0.95`. Also available in AI Studio, MiMo Code, Xiaomi MiMo Desktop, Xiaomi MiMo Open Platform API, and OpenRouter. ## Citation ```bibtex @misc{mimo2026v26flash, title={MiMo-V2.6-Flash-RL}, author={{Xiaomi MiMo Team}}, year={2026}, howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL}}, } ``` ## Contact For questions or feedback, reach us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com) or join our community: - [WeChat Group](https://work.weixin.qq.com/apph5/external_room/join/group_mng?plg_id=c417f99bd9014b5dd894daa8bfe19790&) - [Discord](https://discord.gg/WX2R2uNp) - [Telegram](https://t.me/+3T-I0pekOVIyNDBl) - [Reddit](https://www.reddit.com/r/XiaomiMiMo_Official/)