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Computer Science > Machine Learning

arXiv:2504.10449 (cs)
[Submitted on 14 Apr 2025]

Title:M1: Towards Scalable Test-Time Compute with Mamba Reasoning
Models

Authors:Junxiong Wang, Wen-Ding Li, Daniele Paliotta, Daniel Ritter,
Alexander M. Rush, Tri Dao
View a PDF of the paper titled M1: Towards Scalable Test-Time Compute
with Mamba Reasoning Models, by Junxiong Wang and 5 other authors
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    Abstract:Effective reasoning is crucial to solving complex
    mathematical problems. Recent large language models (LLMs) have
    boosted performance by scaling test-time computation through long
    chain-of-thought reasoning. However, transformer-based models are
    inherently limited in extending context length due to their
    quadratic computational complexity and linear memory
    requirements. In this paper, we introduce a novel hybrid linear
    RNN reasoning model, M1, built on the Mamba architecture, which
    allows memory-efficient inference. Our approach leverages a
    distillation process from existing reasoning models and is
    further enhanced through RL training. Experimental results on the
    AIME and MATH benchmarks show that M1 not only outperforms
    previous linear RNN models but also matches the performance of
    state-of-the-art Deepseek R1 distilled reasoning models at a
    similar scale. We also compare our generation speed with a highly
    performant general purpose inference engine, vLLM, and observe
    more than a 3x speedup compared to a same size transformer. With
    throughput speedup, we are able to achieve higher accuracy
    compared to DeepSeek R1 distilled transformer reasoning models
    under a fixed generation time budget using self-consistency
    voting. Overall, we introduce a hybrid Mamba reasoning model and
    provide a more effective approach to scaling test-time generation
    using self-consistency or long chain of thought reasoning.

Comments: Code is available this https URL
Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2504.10449 [cs.LG]
          (or arXiv:2504.10449v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2504.10449
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Junxiong Wang [view email]
[v1] Mon, 14 Apr 2025 17:38:25 UTC (105 KB)
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