This article presents EUREKA, a human-level reward design algorithm powered by Large Language Models (LLMs). It employs LLMs’ zero-shot generation and code-writing abilities to perform evolutionary optimization over reward code….
Continue readingThe researchers from Peking University have proposed a novel algorithm, Safe Reinforcement Learning from Human Feedback (Safe RLHF), aimed at enhancing the safety and performance of Large Language Models (LLMs)….
Continue readingThe paper, ‘Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning’ by Yunlong Song et al., presents a study comparing the effectiveness of Reinforcement Learning (RL) and Optimal…
Continue readingThe study focuses on off-policy evaluation (OPE) in reinforcement learning using human preference data. The authors explore the sample efficiency of OPE, establishing a statistical guarantee for it. They approach…
Continue readingThe paper focuses on the issue of overoptimization in large language models (LLMs) which are optimized to align with human preferences. The authors highlight that human preferences are multi-faceted and…
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
