RARL: Improving Medical VLM Reasoning and Generalization with Reinforcement Learning and LoRA under Data and Hardware Constraints (2506.06600v2)

Abstract: The growing integration of vision-LLMs (VLMs) in medical applications offers promising support for diagnostic reasoning. However, current medical VLMs often face limitations in generalization, transparency, and computational efficiency-barriers that hinder deployment in real-world, resource-constrained settings. To address these challenges, we propose a Reasoning-Aware Reinforcement Learning framework, \textbf{RARL}, that enhances the reasoning capabilities of medical VLMs while remaining efficient and adaptable to low-resource environments. Our approach fine-tunes a lightweight base model, Qwen2-VL-2B-Instruct, using Low-Rank Adaptation and custom reward functions that jointly consider diagnostic accuracy and reasoning quality. Training is performed on a single NVIDIA A100-PCIE-40GB GPU, demonstrating the feasibility of deploying such models in constrained environments. We evaluate the model using an LLM-as-judge framework that scores both correctness and explanation quality. Experimental results show that RARL significantly improves VLM performance in medical image analysis and clinical reasoning, outperforming supervised fine-tuning on reasoning-focused tasks by approximately 7.78%, while requiring fewer computational resources. Additionally, we demonstrate the generalization capabilities of our approach on unseen datasets, achieving around 27% improved performance compared to supervised fine-tuning and about 4% over traditional RL fine-tuning. Our experiments also illustrate that diversity prompting during training and reasoning prompting during inference are crucial for enhancing VLM performance. Our findings highlight the potential of reasoning-guided learning and reasoning prompting to steer medical VLMs toward more transparent, accurate, and resource-efficient clinical decision-making. Code and data are publicly available.

An Examination of RARL: Enhancing Medical VLMs with Reinforcement Learning and LoRA

This paper discusses a novel framework known as the Reasoning-Aware Reinforcement Learning (RARL) framework, developed to enhance the performance of medical vision-LLMs (VLMs) in constrained environments. The focus is on optimizing both the reasoning ability and generalization capabilities of medical VLMs under limited data and hardware conditions, a crucial advancement in the field of artificial intelligence applications in medical diagnostics.

The authors acknowledge that existing medical VLMs often encounter limitations in terms of generalization, transparency, and efficiency. These challenges are paramount in a real-world scenario where resource constraints are prevalent, particularly in smaller healthcare settings. The RARL framework addresses these challenges through targeted fine-tuning of a base model, Qwen2-VL-2B-Instruct, using strategies such as Low-Rank Adaptation (LoRA) and custom reward functions in a reinforcement learning (RL) setup.

Methodological Insights

The paper employs a Low-Rank Adaptation (LoRA) strategy coupled with reinforcement learning to fine-tune a lightweight medical VLM. LoRA reduces computational intensity by decreasing the number of trainable parameters, permitting the model to be trained on a single NVIDIA A100 GPU, which underscores the feasibility of the approach in resource-constrained setups. This is contrasted with previous methods that typically demand extensive computational resources.

The experimental results demonstrated improvements in diagnostic and reasoning tasks. The RARL framework showed a 7.78% enhancement over supervised fine-tuning in reasoning-focused tasks and a 27% improvement on unseen datasets compared to conventional methods, signifying substantial strides in generalization. The reinforcement learning paradigm facilitated this by optimizing models with a Group Relative Policy Optimization (GRPO) strategy, a critical advance over traditional RL methods.

Dataset and Evaluation

The researchers developed and utilized a reasoning-focused dataset, augmented with diverse prompting strategies during training, which strengthens the model’s ability to adapt to a range of clinical scenarios. Evaluation was performed using an LLM-as-judge framework, further exploring both the correctness of the answer and the underlying reasoning — a holistic approach that reflects the nuanced understanding necessary in medical diagnostics.

Implications and Future Directions

The implications of this research are significant, showcasing the practicality of deploying sophisticated AI models in diverse clinical settings, particularly where computational resources are limited. These advancements pave the way for more transparent and accurate decision-making aids in medicine, providing additional support to healthcare professionals in interpreting complex diagnostic imagery.

Despite the promising results, the paper recognizes certain limitations, such as the model’s propensity to hallucinate or produce generic responses in complex medical scenarios. Future research is likely to address these limitations by extending the range and diversity of training datasets and possibly integrating more advanced, domain-specific prompts to better align model predictions with clinical realities.

In conclusion, the integration of reasoning-aware reinforcement learning strategies with efficient training techniques like LoRA signals meaningful progress in the practical application of AI in healthcare. This model can serve as a template for future developments aimed at enhancing the speed and reliability of medical VLMs while steering clear of the pitfalls associated with high computational demands and limited dataset generalization.