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This research aims to enhance the capabilities of vision-language models in interpreting chest X-rays by utilizing reinforcement learning techniques.

February 13, 2026Open Access

Enhancing Radiology Report Generation and Visual Grounding using Reinforcement Learning

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Reinforcement learning improves report generation in chest X-ray models, suggesting a powerful approach for medical AI.

This research aims to enhance the capabilities of vision-language models in interpreting chest X-rays by utilizing reinforcement learning techniques.
Conducted large-scale supervised fine-tuning on chest X-ray data to create an updated model (RadVLM)
Implemented cold-start supervised fine-tuning to introduce basic reasoning capabilities
Applied Group Relative Policy Optimization with task-specific rewards for report generation and visual grounding
Conducted matched experiments on various Qwen3-VL model variants, assessing the impact of reinforcement learning and reasoning
Reinforcement learning improved performance on report generation and visual grounding tasks compared to baseline models
Strong supervised fine-tuning remains essential for initial high performance
Explicit reasoning did not enhance outcomes further in reinforcement learning settings
RadVLM models achieved state-of-the-art performance in all evaluated areas

Gundersen et al. (2025) studied this question.

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February 13, 2026Open Access

View Full Paper

Ask AI

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Authors

Reinforcement learning improves report generation in chest X-ray models, suggesting a powerful approach for medical AI.

This research aims to enhance the capabilities of vision-language models in interpreting chest X-rays by utilizing reinforcement learning techniques.
Conducted large-scale supervised fine-tuning on chest X-ray data to create an updated model (RadVLM)
Implemented cold-start supervised fine-tuning to introduce basic reasoning capabilities
Applied Group Relative Policy Optimization with task-specific rewards for report generation and visual grounding
Conducted matched experiments on various Qwen3-VL model variants, assessing the impact of reinforcement learning and reasoning
Reinforcement learning improved performance on report generation and visual grounding tasks compared to baseline models
Strong supervised fine-tuning remains essential for initial high performance
Explicit reasoning did not enhance outcomes further in reinforcement learning settings
RadVLM models achieved state-of-the-art performance in all evaluated areas

Gundersen et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark