Advancing Multimodal Reasoning via Reinforcement Learning with Cold
  Start
Authors: name: Lai Wei, name: Yuting Li, name: Kaipeng Zheng, name: Chen Wang, name: Yue Wang, name: Linghe Kong, name: Lichao Sun, name: Weiran Huang

Recent advancements in large language models (LLMs) have demonstrated
impressive chain-of-thought reasoning capabilities, with reinforcement learning
(RL) playing a crucial role in this progress. While "aha moment"
patterns--where models exhibit self-correction through reflection--are often
attributed to emergent properties from RL, we first demonstrate that these
patterns exist in multimodal LLMs (MLLMs) prior to RL training but may not
necessarily correlate with improved reasoning performance. Building on these
insights, we present a comprehensive study on enhancing multimodal reasoning
through a two-stage approach: (1) supervised fine-tuning (SFT) as a cold start
with structured chain-of-thought reasoning patterns, followed by (2)
reinforcement learning via GRPO to further refine these capabilities. Our
extensive experiments show that this combined approach consistently outperforms
both SFT-only and RL-only methods across challenging multimodal reasoning
benchmarks. The resulting models achieve state-of-the-art performance among
open-source MLLMs at both 3B and 7B scales, with our 7B model showing
substantial improvements over base models (e.g., 66.3 %rightarrow73.4 % on
MathVista, 62.9 %rightarrow70.4 % on We-Math) and our 3B model achieving
performance competitive with several 7B models. Overall, this work provides
practical guidance for building advanced multimodal reasoning models. Our code
is available at https://github.com/waltonfuture/RL-with-Cold-Start.