Tag
This paper presents a reinforcement learning post-training pipeline for tool-calling LLM agents operating on FHIR healthcare data, achieving a 77% answer correctness on FHIR-AgentBench using a smaller Qwen3-8B model compared to 50% with o4-mini.
The article discusses the trade-offs between ReAct and CodeAct orchestration paradigms in AI engineering, highlighting CodeAct's efficiency for complex tasks and introducing a new open-source framework.