XiaomiMiMo/MiMo-V2.6-Pro-RL — new model trending #30 on Hugging Face

- **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 *(at release)* |

| **MiMo-V2.6-Flash-RL** | [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL) · 🤖 ModelScope *(at release)* |

## 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 \

Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL