# Fine-Tuning a Personalized OpenBioLLM Using Offline Reinforcement Learning
## 1 Introduction  
The field of personalized medicine has witnessed significant advancements over the past decade, driven by the recognition that individual variability in genetics, environment, and lifestyle profoundly influences health outcomes. Personalized treatment, which tailors medical interventions to the unique characteristics of each patient, has emerged as a cornerstone of modern healthcare. This shift from standardized protocols to individualized care has demonstrated substantial benefits, including improved therapeutic efficacy, reduced adverse effects, and enhanced patient satisfaction. However, implementing personalized treatment at scale remains a formidable challenge due to the high labor costs and time-intensive nature of individual patient assessment by clinicians. The need to bridge this gap has sparked interest in leveraging artificial intelligence (AI) to support and enhance the delivery of personalized care [1].  
Recent advancements in AI, particularly the development of large language models (LLMs) such as GPT-4 [2] and beyond, have shown promise in revolutionizing healthcare. These models, trained on vast amounts of data, are capable of performing a variety of complex tasks, including medical text generation, clinical note summarization, and even answering patient queries. More importantly, they hold the potential to act as intelligent agents capable of analyzing patient data and generating personalized treatment plans. Despite their potential, the application of LLMs in clinical decision making remains in its infancy. They often lack the precision required for medical decision making and struggle to optimize treatment strategies in complex, high-stakes scenarios. These shortcomings highlight the need for targeted improvements to adapt LLMs to the intricacies of clinical care.  
To improve the clinical utility of LLM [3], we explored advanced machine learning techniques, combining representation learning [4], inverse reinforcement learning (IRL) [5], and offline reinforcement learning (ORL) [6] techniques. Representation learning aims to discover meaningful patterns and features from raw historical data, providing state representations for reinforcement learning, so that the model can better understand the estimated relationship between patient characteristics and treatment outcomes. In addition, IRL attempts to infer the underlying goals of clinician decisions. By learning the implicit goals of physician behavior, the model can go beyond imitation and begin to optimize treatment strategies. ORL complements this approach by allowing the model to learn from pre-existing datasets and derive real-time interactions, which is particularly valuable in the healthcare field because real-time exploration may bring ethical and practical challenges.  
By combining the above technologies, we proposed an innovative method for fine-tuning the medical large language model. Through research on the mechanical ventilation treatment dataset constructed in the MIMIC-III database [7], we found that the fine-tuned medical large language model can optimize treatment strategies, achieve accuracy of treatment recommendations, and generate personalized treatment plans.