AINeutralarXiv – CS AI · 9h ago6/10
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POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
Researchers introduce POETS, a novel framework that optimizes large language models through compute-efficient policy ensembles while quantifying uncertainty. By leveraging KL-regularized Thompson sampling and shared backbone architectures with independent LoRA branches, POETS achieves superior sample efficiency in scientific discovery tasks while reducing computational overhead compared to traditional ensemble methods.