Model Alignment

Model alignment focuses on ensuring that large language models (LLMs) behave as intended, aligning their outputs with human values and preferences. Current research emphasizes improving reward models through techniques like reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO), often incorporating more nuanced feedback than simple binary preferences. These advancements aim to mitigate issues like format bias, safety vulnerabilities arising from fine-tuning, and the propagation of errors from unreliable training data, ultimately leading to more reliable and trustworthy AI systems. The impact of this work is significant, improving the safety and usability of LLMs across diverse applications, from medical decision support to code generation.