The field of large language models (LLMs) is moving towards more effective and interpretable models for complex clinical reasoning tasks. Recent developments have focused on adapting LLMs to healthcare contexts using reinforcement learning and knowledge editing techniques. These approaches aim to improve the accuracy and transparency of LLMs in medical applications, such as disease diagnosis and patient-trial matching. Notable advancements include the development of frameworks for evaluating medical knowledge editing and the proposal of novel methods for eliminating shortcut learning in knowledge editing. Noteworthy papers include:

• Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning, which presents a practical recipe for adapting LLMs to complex clinical reasoning tasks.
• Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains, which investigates step-by-step reasoning in medical and mathematical domains.
• Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing, which proposes a novel framework for evaluating medical knowledge editing methods.
• Unveiling and Eliminating the Shortcut Learning for Locate-Then-Edit Knowledge Editing via Both Subject and Relation Awareness, which proposes a two-stage optimization process to eliminate shortcut learning in knowledge editing.
• Efficient Knowledge Editing via Minimal Precomputation, which shows that knowledge editing can be performed with significantly reduced precomputation time.