M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models
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.
67fe15543aa5a5684ca7229e
Junxiong Wang
admin_assigned
2025-04-15T08:35:36.587Z
63dc68bea99b2c8a7c20f1d0
/avatars/cf8ddc91415ef1f895803f4390ff1f6f.svg
Junxiong Wang
user
JunxiongWang
67fe15543aa5a5684ca7229f
Wen-Ding Li
admin_assigned
2025-04-15T08:35:44.671Z
62e221dfcb1f164f2cb8a66b
/avatars/06f05622e232304d3f0b8c291f3263be.svg
Wen-Ding Li
user
xu3kev
67fe15543aa5a5684ca722a0
Daniele Paliotta
67fe15543aa5a5684ca722a1
Daniel Ritter
67fe15543aa5a5684ca722a2
Alexander M. Rush
67fe15543aa5a5684ca722a3
Tri Dao
admin_assigned
2025-04-15T08:36:25.942Z
64b8a6b5cf14c2fabe98159b
/avatars/dbc009451865435bf290791beadc4723.svg
Tri Dao
user
tridao
M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models
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.
A hybrid linear RNN reasoning model, M1, based on the Mamba architecture, achieves memory-efficient inference and outperforms other models, including DeepSeek R1, with faster generation speed and higher accuracy.
LLMs chain-of-thought reasoning transformer-based models computational complexity memory requirements hybrid linear RNN Mamba architecture distillation process RL training AIME MATH benchmarks Deepseek R1 vLLM self-consistency voting