Jahid Hasan Polash (B.Sc.)

Europe’s healthcare must improve interoperability and embrace solutions to unlock the value of legacy clinical data. We used LLMs to transform unstructured clinical reports into structured records. We built a complete workflow, including a UI, and benchmarked various LLM sizes through both prompt engineering and fine-tuning. Our fine-tuned smaller models matched or even surpassed the larger ones, making them ideal for settings with limited computational resources. Finally, we validated a novel dataset of annotated English and German translations of clinical summaries using automated metrics alongside expert manual review.

Large Language Models (LLMs) have been developed for a wide range of language-based tasks, while Reinforcement Learning (RL) has been primarily applied to decision-making problems such as robotics, game theory, and control systems. Nowadays, these two paradigms are integrated through different synergies. In this literature review, we focus on \textit{RL4LLM fine-tuning}, where RL techniques are systematically leveraged to fine-tune LLMs and align them with various preferences. Our review provides a comprehensive analysis of 230 recent publications, presenting a methodological taxonomy that organizes current research into three primary method domains: \textit{Optimization Algorithm}, concerning innovation in core RL update rules; \textit{Training Framework}, regarding innovation in the orchestration of the training process; and \textit{Reward Modeling}, addressing how LLMs learn and represent preferences and feedback. Within these primary domains, we further analyze methods and innovations through more granular categories to provide an in-depth summary of RL4LLM fine-tuning research. We address three research questions: 1) recent methods overview, 2) methodological innovations, and 3) limitations and future directions. Our analysis comprehensively demonstrates the breadth and impact of recent RL4LLM fine-tuning research while highlighting valuable directions for future investigation.