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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2512.23090 |
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| _version_ | 1866908743198834688 |
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| author | Berger, Armin Bergau, Manuela Schneider, Helen Ahmad, Saad Lagones, Tom Anglim Brugnara, Gianluca Foltyn-Dumitru, Martha Schlamp, Kai Vollmuth, Philipp Sifa, Rafet |
| author_facet | Berger, Armin Bergau, Manuela Schneider, Helen Ahmad, Saad Lagones, Tom Anglim Brugnara, Gianluca Foltyn-Dumitru, Martha Schlamp, Kai Vollmuth, Philipp Sifa, Rafet |
| contents | Recent Reinforcement Learning (RL) advances for Large Language Models (LLMs) have improved reasoning tasks, yet their resource-constrained application to medical imaging remains underexplored. We introduce ChexReason, a vision-language model trained via R1-style methodology (SFT followed by GRPO) using only 2,000 SFT samples, 1,000 RL samples, and a single A100 GPU. Evaluations on CheXpert and NIH benchmarks reveal a fundamental tension: GRPO recovers in-distribution performance (23% improvement on CheXpert, macro-F1 = 0.346) but degrades cross-dataset transferability (19% drop on NIH). This mirrors high-resource models like NV-Reason-CXR-3B, suggesting the issue stems from the RL paradigm rather than scale. We identify a generalization paradox where the SFT checkpoint uniquely improves on NIH before optimization, indicating teacher-guided reasoning captures more institution-agnostic features. Furthermore, cross-model comparisons show structured reasoning scaffolds benefit general-purpose VLMs but offer minimal gain for medically pre-trained models. Consequently, curated supervised fine-tuning may outperform aggressive RL for clinical deployment requiring robustness across diverse populations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmark Success, Clinical Failure: When Reinforcement Learning Optimizes for Benchmarks, Not Patients Berger, Armin Bergau, Manuela Schneider, Helen Ahmad, Saad Lagones, Tom Anglim Brugnara, Gianluca Foltyn-Dumitru, Martha Schlamp, Kai Vollmuth, Philipp Sifa, Rafet Artificial Intelligence Machine Learning Recent Reinforcement Learning (RL) advances for Large Language Models (LLMs) have improved reasoning tasks, yet their resource-constrained application to medical imaging remains underexplored. We introduce ChexReason, a vision-language model trained via R1-style methodology (SFT followed by GRPO) using only 2,000 SFT samples, 1,000 RL samples, and a single A100 GPU. Evaluations on CheXpert and NIH benchmarks reveal a fundamental tension: GRPO recovers in-distribution performance (23% improvement on CheXpert, macro-F1 = 0.346) but degrades cross-dataset transferability (19% drop on NIH). This mirrors high-resource models like NV-Reason-CXR-3B, suggesting the issue stems from the RL paradigm rather than scale. We identify a generalization paradox where the SFT checkpoint uniquely improves on NIH before optimization, indicating teacher-guided reasoning captures more institution-agnostic features. Furthermore, cross-model comparisons show structured reasoning scaffolds benefit general-purpose VLMs but offer minimal gain for medically pre-trained models. Consequently, curated supervised fine-tuning may outperform aggressive RL for clinical deployment requiring robustness across diverse populations. |
| title | Benchmark Success, Clinical Failure: When Reinforcement Learning Optimizes for Benchmarks, Not Patients |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.23090 |