Brief

A new research paper explores the trade-offs between performance, energy consumption, and privacy when running large language models on mobile devices. The study developed an experimental pipeline to measure these factors on an Android device, testing eight LLMs. Findings indicate that model architecture, rather than quantization, is key for energy efficiency, with Mixture-of-Experts models showing promise for balancing storage and power usage. AI

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