LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through
  Two-Stage Rule-Based RL
Authors: name: Yingzhe Peng, name: Gongrui Zhang, name: Miaosen Zhang, name: Zhiyuan You, name: Jie Liu, name: Qipeng Zhu, name: Kai Yang, name: Xingzhong Xu, name: Xin Geng, name: Xu Yang

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.