Instructions to use XiaomiMiMo/MiMo-V2.6-Pro-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
-  Transformers How to use XiaomiMiMo/MiMo-V2.6-Pro-RL with Transformers: # Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-V2.6-Pro-RL", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("XiaomiMiMo/MiMo-V2.6-Pro-RL", trust_remote_code=True, device_map="auto")
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
-  vLLM  How to use XiaomiMiMo/MiMo-V2.6-Pro-RL with vLLM: Install from pip and serve model# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiaomiMiMo/MiMo-V2.6-Pro-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2.6-Pro-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'Use Dockerdocker model run hf.co/XiaomiMiMo/MiMo-V2.6-Pro-RL
-  SGLang  How to use XiaomiMiMo/MiMo-V2.6-Pro-RL with SGLang: Install from pip and serve model# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "XiaomiMiMo/MiMo-V2.6-Pro-RL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2.6-Pro-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'Use Docker imagesdocker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "XiaomiMiMo/MiMo-V2.6-Pro-RL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2.6-Pro-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
-  Docker Model Runner  How to use XiaomiMiMo/MiMo-V2.6-Pro-RL with Docker Model Runner: docker model run hf.co/XiaomiMiMo/MiMo-V2.6-Pro-RL
MiMo-V2.6-Pro-RL
Scaling Reinforcement Learning Toward Self-Improvement
1. Introduction
MiMo-V2.6-Pro-RL is the flagship checkpoint of the MiMo-V2.6 series. The series is built to scale reinforcement learning toward self-improvement — 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 — 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 — 1,568 prompts × 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 — 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 — extending capabilities to hard-to-verify tasks.
Model Summary
- Architecture: Sparse MoE (Mixture of Experts), 1.02T total / 42B 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.
2. Downloads
| Model | Download |
|---|---|
| MiMo-V2.6-Pro-RL | 🤗 HuggingFace · 🤖 ModelScope |
| MiMo-V2.6-Flash-RL | 🤗 HuggingFace · 🤖 ModelScope |
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-Pro-RL |
|---|---|
| Layers (Total / SWA / GA) | 70 / 60 / 10 |
| Hidden Size | 6144 |
| SWA Heads (Q/KV) | 128 / 8 |
| GA Heads (Q/KV) | 128 / 8 |
| Head Dimensions (QK / V) | 192 / 128 |
| Sliding Window Size | 128 |
| Routed Experts (Total / Activated) | 384 / 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 × H × W) | 2 × 16 × 16 |
| Sliding Window (Left / Right) | 64 / 64 |
| Spatial Merge Size | 2 × 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 → 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. Docker image: lmsysorg/sglang:latest.
SGLang
sglang serve \
  --trust-remote-code \
  --model-path XiaomiMiMo/MiMo-V2.6-Pro-RL \
  --tp 16 \
  --dp 2 \
  --enable-dp-attention \
  --mm-enable-dp-encoder \
  --ep 16 \
  --moe-a2a-backend deepep \
  --moe-dense-tp-size 1 \
  --mem-fraction-static 0.7 \
  --max-running-requests 128 \
  --chunked-prefill-size 32768 \
  --page-size 64 \
  --swa-full-tokens-ratio 0.3 \
  --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 \
  --nnodes 2 \
  --node-rank <node-rank> \
  --dist-init-addr <node0-ip>:20000
vLLM
Follow the vLLM MiMo-V2.5 recipe. Pre-built image: docker pull vllm/vllm-openai:mimov25-cu129.
vllm serve XiaomiMiMo/MiMo-V2.6-Pro-RL \
  --tensor-parallel-size 8 \
  --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
@misc{mimo2026v26pro,
  title={MiMo-V2.6-Pro-RL},
  author={{Xiaomi MiMo Team}},
  year={2026},
  howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL}},
}
Contact
For questions or feedback, reach us at mimo@xiaomi.com or join our community:
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