On the Suitability of Reinforcement Fine-Tuning to Visual Tasks

2025-04-08

AI Summary

This work investigates the effectiveness and applicability boundaries of Reinforcement Fine-Tuning (RFT) for visual understanding in Multimodal Large Language Models (MLLMs). Method: Through systematic evaluation across diverse visual benchmarks, we compare RFT against Supervised Fine-Tuning (SFT) and propose a novel “reasoning-encouraging” reward mechanism that modulates reasoning depth. Contribution/Results: We empirically establish RFT’s general superiority—particularly in low-data regimes—while revealing its task-dependent performance: gains diminish with increasing task complexity. Crucially, we find that moderate reasoning depth enhances performance on complex tasks but harms accuracy on simpler ones. Our study thus delineates the operational boundaries of RFT for vision-centric tasks and introduces an interpretable, task-adaptive reinforcement learning paradigm for optimizing multimodal reasoning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Reinforcement Fine-Tuning (RFT) is proved to be greatly valuable for enhancing the reasoning ability of LLMs. Researchers have been starting to apply RFT to MLLMs, hoping it will also enhance the capabilities of visual understanding. However, these works are at a very early stage and have not examined how suitable RFT actually is for visual tasks. In this work, we endeavor to understand the suitabilities and limitations of RFT for visual tasks, through experimental analysis and observations. We start by quantitative comparisons on various tasks, which shows RFT is generally better than SFT on visual tasks. %especially when the number of training samples are limited. To check whether such advantages are brought up by the reasoning process, we design a new reward that encourages the model to ``think'' more, whose results show more thinking can be beneficial for complicated tasks but harmful for simple tasks. We hope this study can provide more insight for the rapid advancements on this topic.

Problem

• Assess RFT suitability for visual tasks
• Compare RFT and SFT performance visually
• Evaluate impact of reasoning on visual tasks

Innovation

• Reinforcement Fine-Tuning enhances visual task performance
• New reward design encourages model reasoning process
• Quantitative comparisons show RFT outperforms SFT