MemReward: Graph-Based Experience Memory for LLM Reward Prediction with Limited Labels
Researchers have developed MemReward, a novel graph-based framework designed to improve reinforcement learning for large language models (LLMs) when labeled data is scarce. This method uses a graph neural network (GNN) to propagate reward signals from a small set of labeled examples to a larger pool of unlabeled data. Experiments show that MemReward can achieve performance close to that of an oracle (fully labeled data) even with only 20% of the data labeled, demonstrating its effectiveness across various tasks like mathematics, question answering, and code generation. AI
IMPACT Enables more efficient fine-tuning of LLMs in data-scarce environments, potentially accelerating development across various AI applications.