This paper introduces the Distributional Preference Reward Model (DPRM) to align large language models with a diverse set of human preferences. The researchers used a beta distribution to characterize preferences,…
Continue readingThe paper focuses on aligning language models with human preferences for real-world applications. It discusses the drawbacks of reinforcement learning (RL) and direct preference optimization (DPO) in achieving this goal….
Continue readingThis article presents a framework for robot task planning that is adaptive to human preferences and performance. The authors focus on task allocation and scheduling in collaborative settings, aiming to…
Continue readingThe study investigates whether incorporating human preferences in Reinforcement Learning (RL) can enhance the transparency of robot behaviours. A shielding mechanism is integrated into the RL algorithm to monitor the…
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…
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