MemReward uses graph neural networks to boost LLM rewards 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.

RANK_REASON The cluster contains an academic paper detailing a new method for LLM reward prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• LLM
• MemReward
• Qwen2.5-1.5B
• Qwen2.5-3B
• Tianyang Luo

• paper
• model release
• infra

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