Alignment Algorithm

Alignment algorithms aim to harmonize the outputs of large language models (LLMs) with human preferences, addressing concerns about undesirable behaviors like toxicity or biases. Current research focuses on improving efficiency and robustness, exploring methods like direct preference optimization (DPO) and advantage alignment, while also investigating the underlying mechanisms of alignment and developing frameworks for evaluating progress, such as ProgressGym. These advancements are crucial for building trustworthy and beneficial AI systems, impacting both the development of safer LLMs and the broader understanding of human-AI interaction.