Model Alignment Process

The alignment of generative models with human feedback has notably enhanced the performance of natural language generation tasks, with methods such as reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) proving more effective than supervised fine-tuning (SFT) alone. These approaches aim to align large language models (LLMs) to produce outputs that match human preferences, thereby preventing the generation of illegal or incorrect content. RLHF, for instance, involves a cycle of data collection, reward modeling, policy optimization, and iterative refinement to align AI behavior with human values. DPO, on the other hand, focuses on adjusting policies based on preference without explicit reward modeling, while methods like Kahneman-Tversky Optimization (KTO) incorporate human psychological biases into the learning process. Additionally, Self-Play Fine-Tuning (SPIN) leverages synthetic data to enhance LLMs without relying on extensive human-annotated data. These methods, coupled with ongoing developments such as ORPO, aim to improve the reliability and usefulness of AI models in line with human expectations.