229 citations · 597 across the 19 of their papers we have counts for
19 papers
Evaluating Criterion-Conditioned Behaviour of Large Language Models in Content Moderation
Danting Zhang, Bei Peng, Robert Loftin
Large language models (LLMs) demonstrate strong performance on standard content moderation benchmarks. However, these benchmarks often aggregate multiple moderation criteria into a…
Online Self-Weighted Fine-Tuning
Haiquan Wen, Yiwei He, Bei Peng +1
Standard supervised fine-tuning (SFT) assigns the same explicit loss weight to every expert demonstration, regardless of the model's changing competence over training queries. Rein…
LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback
Manith Adikari, Bei Peng, Samuele Vinanzi +1
Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function. However, real-world decision-making tasks often involve multiple, c…
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility
Flora Charbonnier, Bei Peng, Thomas Morstyn +1
This paper investigates how deep multi-agent reinforcement learning can enable the scalable and privacy-preserving coordination of residential energy flexibility. The coordination…
CURO: Curriculum Learning for Relative Overgeneralization
Lin Shi, Qiyuan Liu, Bei Peng
Relative overgeneralization (RO) is a pathology that can arise in cooperative multi-agent tasks when the optimal joint action's utility falls below that of a sub-optimal joint acti…
Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping
Gabriella Pizzuto, Hetong Wang, Hatem Fakhruldeen +3
The use of laboratory robotics for autonomous experiments offers an attractive route to alleviate scientists from tedious tasks while accelerating material discovery for topical is…