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Scaling Reinforcement Learning Toward Self-Improvement
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:
[Image blocked: Figure 1: MiMo-V2.6 architecture — omni encoders, hybrid SWA backbone, and MTP blocks]
Figure 1. MiMo-V2.6 architecture.
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.
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).
5-layer SWA MTP drafter (DFlash-style). Predicts 7 subsequent tokens per forward pass for parallel verification.
For best performance, follow the SGLang MiMo cookbook. Docker image: lmsysorg/sglang:latest.
Follow the vLLM MiMo-V2.5 recipe. Pre-built image: docker pull vllm/vllm-openai:mimov25-cu129.
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.
For questions or feedback, reach us at mimo@xiaomi.com or join our community:
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>:20000vllm 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@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}},}