Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuning

ArXi:2511.21075v2 Announce Type: replace-cross Aligning Large Language Models (LLMs) with biomedical knowledge requires understanding both concepts and causal mechanisms in scientific reports. Supervised Fine-Tuning (SFT) often fails to capture these logical structures, while Reinforcement Learning (RL) is limited by sparse reward signals. We propose Balanced Fine-Tuning (BFT), a dual-scale post-