Self-Evolved Reward Learning for LLMs

Reinforcement Learning from Human Feedback (RLHF) is a crucial technique for aligning language models with human preferences, playing a pivotal role in the success of conversational models like GPT-4, ChatGPT, and Llama 2. A core challenge in employing RLHF lies in training a reliable reward model (RM), which relies on high-quality labels typically provided by human experts or advanced AI system. These methods can be costly and may introduce biases that affect the language model's responses. As language models improve, human input may become less effective in further enhancing their performance. In this paper, we propose Self-Evolved Reward Learning (SER), a novel approach where the RM generates additional training data to iteratively improve itself. We conducted extensive experiments on multiple datasets such as HH-RLHF and UltraFeedback, using models like Mistral and Llama 3, and compare SER against various baselines. Our results demonstrate that even with limited human-annotated data, learning from self-feedback can robustly enhance RM performance, thereby boosting the capabilities of large language models (LLMs). Reinforcement Learning from Human Feedback (RLHF) is a well-established approach that aligns Large Language Models (LLMs) with human preference data Ouyang et al. (2022); Bai et al. (2022b). The standard approach involves learning a reward model (RM) from human preferences and the learned RM is then frozen to train LLMs via Reinforcement Learning (RL) such as Proximal Policy Optimization (PPO) Schulman et al. (2017a). Another common approach directly trains LLMs from the human preference data without learning an RM such as Direct Preference Optimiztion (DPO) Rafailov et al. (2024). Both approaches rely heavily on the size and quality of human-annotated preference data. However, the availability of such data is often limited and expensive to acquire, posing a significant bottleneck in the development and performance of RL approaches Yuan et al. (2024b). This dependency on human-annotated data hinders the scalability of strong LLMs that require vast amounts of labeled data to achieve greater performance Kaplan et al. (2020); Muennighoff et al. (2024). To mitigate the dependency, recent works leverage the AI feedback to train RMs, referred to as Reinforcement Learning from AI Feedback (RLAIF) Bai et al. (2022b); Lee et al. (2023), which reduces the reliance on human-annotated data.