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arxiv: 2601.02780 · v2 · submitted 2026-01-06 · 💻 cs.CL · cs.AI

MiMo-V2-Flash Technical Report

Xiaomi LLM-Core Team: Bangjun Xiao , Bingquan Xia , Bo Yang , Bofei Gao , Bowen Shen , Chen Zhang , Chenhong He , Chiheng Lou
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Fuli Luo Gang Wang Gang Xie Hailin Zhang Hanglong Lv Hanyu Li Heyu Chen Hongshen Xu Houbin Zhang Huaqiu Liu Jiangshan Duo Jianyu Wei Jiebao Xiao Jinhao Dong Jun Shi Junhao Hu Kainan Bao Kang Zhou Lei Li Liang Zhao Linghao Zhang Peidian Li Qianli Chen Shaohui Liu Shihua Yu Shijie Cao Shimao Chen Shouqiu Yu Shuo Liu Tianling Zhou Weijiang Su Weikun Wang Wenhan Ma Xiangwei Deng Bohan Mao Bowen Ye Can Cai Chenghua Wang Chengxuan Zhu Chong Ma Chun Chen Chunan Li Dawei Zhu Deshan Xiao Dong Zhang Duo Zhang Fangyue Liu Feiyu Yang Fengyuan Shi Guoan Wang Hao Tian Hao Wu Heng Qu Hongfei Yi Hongxu An Hongyi Guan Xing Zhang Yifan Song Yihan Yan Yihao Zhao Yingchun Lai Yizhao Gao Yu Cheng Yuanyuan Tian Yudong Wang Zhen Tang Zhengju Tang Zhengtao Wen Zhichao Song Zhixian Zheng Zihan Jiang Jian Wen Jiarui Sun Jiawei Li Jinlong Xue Jun Xia Kai Fang Menghang Zhu Nuo Chen Qian Tu Qihao Zhang Qiying Wang Rang Li Rui Ma Shaolei Zhang Shengfan Wang Shicheng Li Shuhao Gu Shuhuai Ren Sirui Deng Tao Guo Tianyang Lu Weiji Zhuang Weikang Zhang Weimin Xiong Wenshan Huang Wenyu Yang Xin Zhang Xing Yong Xu Wang Xueyang Xie Yilin Jiang Yixin Yang Yongzhe He Yu Tu Yuanliang Dong Yuchen Liu Yue Ma Yue Yu Yuxing Xiang Zhaojun Huang Zhenru Lin Zhipeng Xu Zhiyang Chen Zhonghua Deng Zihan Zhang Zihao Yue
This is my paper

Pith reviewed 2026-05-12 11:27 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords Mixture of Expertsmulti-token predictionspeculative decodingdistillationhybrid attentionlanguage modelparameter efficiencyopen-source model
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The pith

MiMo-V2-Flash matches top open-weight models like DeepSeek-V3.2 using half their total parameters via sparse MoE design.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents MiMo-V2-Flash as a Mixture-of-Experts model with 309 billion total parameters but only 15 billion active during use. It pre-trains on 27 trillion tokens with multi-token prediction and a hybrid attention setup that alternates sliding windows with global attention, then extends context from 32k to 256k. A new Multi-Teacher On-Policy Distillation method lets specialized teachers supply token-level guidance so the student fully acquires their capabilities. This enables the model to rival larger systems while delivering inference speedups by reusing its prediction layers as a draft model for speculative decoding.

Core claim

MiMo-V2-Flash is a Mixture-of-Experts model with 309B total parameters and 15B active parameters that rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2 despite using only half or one-third their total parameters. It adopts a hybrid attention architecture interleaving Sliding Window Attention with global attention under a 5:1 ratio and a 128-token window, pre-trains with Multi-Token Prediction on 27 trillion tokens, and introduces Multi-Teacher On-Policy Distillation where domain-specialized teachers provide dense token-level rewards. The model extends to 256k context and repurposes MTP layers for speculative decoding to reach up to 3.6 acceptance length and 2.6x speedup,开放

What carries the argument

Mixture-of-Experts architecture with 15B active parameters out of 309B total, supported by Multi-Teacher On-Policy Distillation that transfers expertise from specialized teachers via token-level rewards.

If this is right

  • The model reaches comparable reasoning and agentic performance to systems with two or three times more total parameters.
  • Inference runs up to 2.6 times faster with 3.6 token acceptance length by treating MTP layers as a speculative draft model.
  • Context length extends to 256k after initial 32k training without separate long-context pre-training.
  • Open release of the model weights and three-layer MTP weights supports community use and further development.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Sparse activation paired with targeted distillation may let future models achieve high capability at lower memory and compute cost during deployment.
  • Hybrid sliding-window and global attention offers a practical balance for long-context tasks that avoids full quadratic scaling.
  • Reusing pre-training prediction heads for inference acceleration could generalize to other auxiliary objectives in language models.

Load-bearing premise

The unreported benchmark results and training details actually demonstrate performance rivaling DeepSeek-V3.2 and Kimi-K2 under comparable conditions.

What would settle it

Independent runs on the same public benchmarks where MiMo-V2-Flash scores noticeably below DeepSeek-V3.2 or Kimi-K2.

read the original abstract

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents MiMo-V2-Flash, a Mixture-of-Experts model with 309B total and 15B active parameters that uses hybrid sliding-window attention (128-token window at 5:1 ratio) interleaved with global attention. It is pre-trained on 27 trillion tokens with multi-token prediction (MTP), extended from 32k to 256k context, and post-trained via a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. The paper claims this model rivals DeepSeek-V3.2 and Kimi-K2 while using only half and one-third their parameters, respectively, and achieves up to 3.6 acceptance length and 2.6x decoding speedup by repurposing MTP layers for speculative decoding. The model and MTP weights are open-sourced.

Significance. If the performance claims hold under matched evaluation conditions, the work would demonstrate practical advances in parameter-efficient MoE scaling for reasoning and agentic capabilities, with the hybrid attention and MOPD methods offering reusable design insights. The open-sourcing of weights and MTP layers would provide immediate value for community replication and further research on speculative decoding.

major comments (2)
  1. [Abstract] Abstract: The claim that MiMo-V2-Flash 'rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively' is unsupported by any benchmark scores, tables, or evaluation details. No side-by-side results on MMLU, GSM8K, HumanEval or similar tasks are supplied, nor is there information on prompting, shot count, or whether baselines were re-evaluated under identical conditions. This is load-bearing for the central contribution.
  2. [Abstract] Abstract: The inference claim of 'up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers' is presented without experimental setup, hardware details, baseline comparisons, or acceptance-length distributions. This prevents assessment of whether the speedup is reproducible or generalizes beyond the reported conditions.
minor comments (2)
  1. [Abstract] The hybrid attention ratio is described as '5:1' without clarifying whether this denotes the fraction of SWA layers, the interleaving pattern, or another quantity; a diagram or explicit definition in the main text would improve clarity.
  2. [Abstract] The context-length extension from native 32k to 256k is mentioned without describing the method (e.g., RoPE scaling factors, continued pre-training schedule, or long-context benchmark results).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation of results and experimental details.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that MiMo-V2-Flash 'rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively' is unsupported by any benchmark scores, tables, or evaluation details. No side-by-side results on MMLU, GSM8K, HumanEval or similar tasks are supplied, nor is there information on prompting, shot count, or whether baselines were re-evaluated under identical conditions. This is load-bearing for the central contribution.

    Authors: We agree that the abstract claim would benefit from direct supporting evidence. The full manuscript contains benchmark tables and evaluation details in the Experiments section, but to make the abstract self-contained we will revise it to include key side-by-side scores on MMLU, GSM8K, HumanEval and related tasks, along with notes on prompting, shot counts, and confirmation that baselines were run under matched conditions. We will also add explicit references to the relevant tables. revision: yes

  2. Referee: [Abstract] Abstract: The inference claim of 'up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers' is presented without experimental setup, hardware details, baseline comparisons, or acceptance-length distributions. This prevents assessment of whether the speedup is reproducible or generalizes beyond the reported conditions.

    Authors: We acknowledge the abstract is overly concise on the inference results. The manuscript includes a dedicated section on speculative decoding that describes the MTP-layer repurposing, hardware setup, baseline autoregressive decoding, and acceptance-length statistics. We will revise the abstract to briefly summarize the experimental conditions, hardware, baseline, and key statistics (including distributions), and ensure the full section provides all reproducibility details. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical technical report with no derivation chain

full rationale

The document is a model release report describing architecture choices (hybrid SWA/global attention, MTP pre-training, MOPD post-training), parameter counts, and benchmark rivalry claims. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. Central performance assertions rest on external benchmark comparisons rather than internal definitions that loop back to the same quantities. The paper is therefore self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claims rest on standard transformer and MoE training assumptions plus the unreported empirical results; no new physical entities or ad-hoc constants are introduced beyond typical hyper-parameters such as the 5:1 attention ratio and 128-token window.

free parameters (2)
  • hybrid attention ratio
    5:1 SWA-to-global ratio chosen to balance speed and quality; value stated without derivation from first principles.
  • sliding window size
    128 tokens; selected for inference efficiency.
axioms (1)
  • domain assumption Standard transformer attention and MoE routing assumptions hold at this scale.
    Invoked implicitly when claiming the architecture trains and runs as described.

pith-pipeline@v0.9.0 · 6053 in / 1376 out tokens · 68802 ms · 2026-05-12T11:27:42.763790+00:00 · methodology

discussion (0)

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