---
license: mit
language:
- en
- zh
tags:
- text-generation
- multimodal
- vision-language
- audio
- agent
- video-understanding
- long-context
- mimo_v2
- transformers
library_name: transformers
---
# MiMo-V2.6-Pro-RL
**Scaling Reinforcement Learning Toward Self-Improvement**
Technical Report
## 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](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) · [🤖 ModelScope](https://www.modelscope.cn/models/XiaomiMiMo/MiMo-V2.6-Pro-RL) |
| **MiMo-V2.6-Flash-RL** | [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL) · [🤖 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-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](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-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 \
--dist-init-addr :20000
```
### vLLM
Follow the [vLLM MiMo-V2.5 recipe](https://recipes.vllm.ai/XiaomiMiMo/MiMo-V2.5). Pre-built image: `docker pull vllm/vllm-openai:mimov25-cu129`.
```bash
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
```bibtex
@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](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/)
## Use Restrictions
You are solely responsible for your use of XiaomiMiMo/MiMo-V2.6-Pro-RL and any outputs, actions, or consequences arising therefrom, and you agree not to use the XiaomiMiMo/MiMo-V2.6-Pro-RL or any derivatives thereof:
- For any unlawful, fraudulent, or malicious purpose, or in any manner that violates any applicable laws or regulations；
- To infringe upon the intellectual property rights, privacy rights, publicity rights, or other lawful rights or interests of any third party;
- To exploit, harm, harass, defame, unlawfully discriminate against, or otherwise adversely affect any individual or group, including minors or vulnerable persons;
- For any military purpose or application;
- To engage in or facilitate malicious cyber activities, including but not limited to malware, ransomware, phishing, credential theft, or unauthorized access, or any activity intended to compromise or unlawfully control computer systems, networks, or encryption systems;
- To access, collect, process, disclose, or disseminate personal data or other protected information without appropriate authorization or lawful basis; or to access, interact with, scrape, or automate any third-party platform or service in violation of any applicable laws, policies, or authorization requirements;
- To use the XiaomiMiMo/MiMo-V2.6-Pro-RL in a manner that autonomously executes high-risk actions without appropriate human oversight or authorization.
This notice is an informational reminder only and does not amend the applicable model licence or impose additional licence conditions. The permissions granted under that licence remain unaffected. Nothing in this notice restricts uses permitted by applicable law, including applicable exceptions and limitations.
### Usage enquiries and complaints
[mimo-legal@xiaomi.com](mailto:mimo-legal@xiaomi.com). For information on submitting a complaint, please visit the https://mimo.xiaomi.com.
