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[!IMPORTANT] This is the MOPD upgrade of the MiMo-V2.6-Pro-RL checkpoint.
- MOPD2 (👉 Technical Report §5.6)
Fuses several domain-specialized teachers into one model, extending to domains where reliable training-time verification is hard, such as long-horizon game development, scientific research and embodied intelligence.- Diagnosing and Mitigating Tool-Call Repetition in MiMo-V2.6 (👉 Technical Blog)
An easy-to-overlook failure mode in which the model keeps issuing the same or highly similar tool calls, appearing busy while making no progress. Nothing fails outright, so it tends to go unnoticed. The MOPD stage handles it efficiently, with a short specialized-teacher run that converges quickly.
MOPD2 distills several domain-specialized teachers into the student on-policy. The teachers fall into two families: mixRL teachers, trained on verifiable tasks, and SFT teachers, trained on synthetic demonstrations for open-domain tasks where a reliable reward is hard to design. Three streams contribute to a single update:
Method details are in Technical Report §5.6.
Following the release of MiMo-V2.6, tool-call repetition emerged as one of the most noticeable issues in agentic settings: the model would sometimes issue the same or highly similar tool calls repeatedly, consuming time and context without making progress. This checkpoint mitigates it.
[Image blocked: Tool-call repetition rate on MiMo-V2.6-Pro before and after MOPD]
Figure: response-level repetition rate on MiMo-V2.6-Pro, RL-stage versus this checkpoint, across context lengths and agent harnesses.
The technical blog has the full diagnosis. The fix is lightweight to train: a short specialized-teacher run that folds into the normal MOPD pass.
[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-MOPD \ --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-MOPD \ --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{mimo2026v26promopd, title={MiMo-V2.6-Pro-MOPD}, author={{Xiaomi MiMo Team}}, year={2026}, howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD}},}