Empowering Lightweight MLLMs with Reasoning via Long CoT SFT

Linyu Ou; YuYang Yin

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arXiv · 2509.03321

Empowering Lightweight MLLMs with Reasoning via Long CoT SFT

Linyu Ou·YuYang Yin

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Abstract

While Reinforcement Learning with Verifiable Rewards has enhanced the reasoning of large-scale language models (LLMs), its efficacy for lightweight multimodal language models (MLLMs) with fewer than seven billion parameters remains underexplored. This paper investigates the role of long Chain-of-Thought (long CoT) data in enhancing the reasoning abilities of such MLLMs. Our findings demonstrate that Supervised Fine-Tuning (SFT) with long CoT data significantly improves MLLM reasoning. Furthermore, we observe that after this initial SFT phase, MLLMs can achieve additional performance gains through a subsequent RL stage. We conclude that a SFT stage with long CoT data is a critical prerequisite for developing the reasoning capabilities of lightweight MLLMs.

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Linyu Ou, YuYang Yin. 2025-09-03. Empowering Lightweight MLLMs with Reasoning via Long CoT SFT. https://arxiv.org/abs/2509.03321

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