The content discusses optimization methods for Large Language Models (LLMs) alignment, focusing on the challenges of ensuring that LLMs act in ways consistent with human values and preferences. It introduces Reinforcement Learning from Human Feedback (RLHF) as a way to fine-tune language models based on direct human feedback, aligning them more closely with human values. The process involves supervised fine-tuning, reward modeling, and RL fine-tuning phases. The reward modeling phase includes training a reward model to generate human preference scores based on model outputs. The RL fine-tuning phase involves using reinforcement learning techniques to update base model parameters based on the reward model’s scores.
Additionally, the content explores a reward-free method in RLHF, which aims to avoid training a new reward model and instead optimize the policy directly from the base model. This method involves training the base model to reach an optimal model policy without the need for a separate reward model. The content delves into the training and feedback loop phases of the reward-free method, comparing it to the reward-based method using Proximal Policy Optimization (PPO). A recent paper compares the two methods and suggests that PPO is generally better than the Direct Preference Optimization (DPO) method, especially when dealing with out-of-distribution data. The research is ongoing to determine which method is superior, with companies like OpenAI, Anthropic, and Meta using both methods for LLM alignment.
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Source link: https://towardsdatascience.com/llm-alignment-reward-based-vs-reward-free-methods-ef0c0f6e8d88