LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through
  Two-Stage Rule-Based RL
Enhancing reasoning in Large Multimodal Models (LMMs) faces unique challenges
from the complex interplay between visual perception and logical reasoning,
particularly in compact 3B-parameter architectures where architectural
constraints limit reasoning capacity and modality alignment.
  While rule-based reinforcement learning (RL) excels in text-only domains, its
multimodal extension confronts two critical barriers: (1) data limitations due
to ambiguous answers and scarce complex reasoning examples, and (2) degraded
foundational reasoning induced by multimodal pretraining.
  To address these challenges, we propose \method, a two-stage
framework adapting rule-based RL for multimodal reasoning through
Foundational Reasoning Enhancement (FRE) followed by
Multimodal Generalization Training (MGT). The FRE stage first
strengthens reasoning abilities using text-only data with rule-based RL, then
the MGT stage generalizes these reasoning capabilities to multimodal domains.
  Experiments on Qwen2.5-VL-Instruct-3B demonstrate that \method achieves
4.83\% and 4.5\% average improvements over baselines in multimodal and
text-only benchmarks, respectively, with a 3.63\% gain in complex Football Game
tasks. These results validate that text-based reasoning enhancement enables
effective multimodal generalization, offering a data-efficient paradigm that
bypasses costly high-quality multimodal training data.
67d04f248f79213c2fc0ba04
Yingzhe Peng
admin_assigned
2025-03-12T15:27:26.416Z
64e82a99ad1bac272b73b5ab
https://cdn-avatars.huggingface.co/v1/production/uploads/64e82a99ad1bac272b73b5ab/AL2bPCy5rrGxG1YrzJ76Z.jpeg
YingzhePeng
user
ColeYzzzz
67d04f248f79213c2fc0ba05
Gongrui Zhang
67d04f248f79213c2fc0ba06
Miaosen Zhang
admin_assigned
2025-03-12T15:25:22.352Z
62a30bf72dac39c2173c0a8c
/avatars/15fb1ea3dcc7ccd8bc8002ce282e27b3.svg
Miaosen Zhang
user
Miaosen
67d04f248f79213c2fc0ba07
Zhiyuan You
admin_assigned
2025-03-12T15:25:53.095Z
64c8f16736c11430f31d9bf6
/avatars/6907df1fedbcedc63046e7457a6b5950.svg
zhiyuanyou
user
zhiyuanyou
67d04f248f79213c2fc0ba08
Jie Liu
67d04f248f79213c2fc0ba09
Qipeng Zhu
67d04f248f79213c2fc0ba0a
Kai Yang
67d04f248f79213c2fc0ba0b
Xingzhong Xu
admin_assigned
2025-03-12T15:26:41.658Z
63216ccd4bf43bca61997ad3
/avatars/45c769b94a39df7b688f50e54d2c6223.svg
Xingzhong Xu
user
Xingzhong
67d04f248f79213c2fc0ba0c
Xin Geng
67d04f248f79213c2fc0ba0d
Xu Yang
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through
  Two-Stage Rule-Based RL
Enhancing reasoning in Large Multimodal Models (LMMs) faces unique challenges
from the complex interplay between visual perception and logical reasoning,
particularly in compact 3B-parameter architectures where architectural
constraints limit reasoning capacity and modality alignment.
  While rule-based reinforcement learning (RL) excels in text-only domains, its
multimodal extension confronts two critical barriers: (1) data limitations due
to ambiguous answers and scarce complex reasoning examples, and (2) degraded
foundational reasoning induced by multimodal pretraining.
  To address these challenges, we propose \method, a two-stage
framework adapting rule-based RL for multimodal reasoning through
Foundational Reasoning Enhancement (FRE) followed by
Multimodal Generalization Training (MGT). The FRE stage first
strengthens reasoning abilities using text-only data with rule-based RL, then
the MGT stage generalizes these reasoning capabilities to multimodal domains.
  Experiments on Qwen2.5-VL-Instruct-3B demonstrate that \method achieves
4.83\% and 4.5\% average improvements over baselines in multimodal and
text-only benchmarks, respectively, with a 3.63\% gain in complex Football Game
tasks. These results validate that text-based reasoning enhancement enables
effective multimodal generalization, offering a data-efficient paradigm that
bypasses costly high-quality multimodal training data.
A two-stage framework enhances reasoning in large multimodal models by first strengthening text-only reasoning and then generalizing it to multimodal tasks.
Large Multimodal Models LMMs rule-based reinforcement learning RL Foundational Reasoning Enhancement FRE Multimodal Generalization Training MGT multimodal reasoning multimodal pretraining