Language Model Self-improvement by Reinforcement Learning Contemplation

摘要

Language model self-improvement (LMSI) techniques have recently gained significant attention as they improve language models without requiring external supervision. A common approach is reinforcement learning from AI feedback (RLAIF), which trains a reward model based on AI preference data and employs a reinforcement learning algorithm to train the language model. However, RLAIF relies on the heuristic assumption that an AI model can provide effective feedback and correct wrong answers, requiring a solid capability of the language model. This paper presents a novel LMSI method, Reinforcement Learning Contemplation (RLC). We disclose that it is simpler for language models to evaluate a sentence than to generate it, even for small language models. Leveraging the gap between the evaluation and generation, RLC evaluates generated answers and updates language model parameters using reinforcement learning to maximize evaluation scores. Through testing on various challenging reasoning tasks and text summarization task, our experiments show that RLC effectively improves language model performance without external supervision, resulting in an answering accuracy increase (from 31.23% to 37.09%) for BigBench-hard reasoning tasks, and a rise in BERTScore for CNN/Daily Mail summarization tasks. Furthermore, RLC can be applied to models of different sizes, showcasing its broad applicability.