Alignment Training

Alignment training aims to make artificial intelligence systems, particularly large language models (LLMs), behave according to human intentions and values. Current research focuses on improving alignment through various techniques, including reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), and novel training strategies that address issues like data scarcity, adversarial attacks, and the trade-off between instruction following and faithfulness. These advancements are crucial for building more trustworthy and beneficial AI systems, impacting fields ranging from natural language processing and autonomous driving to broader applications requiring safe and reliable AI agents.