3 papers
cs.LG2026
Agentic Reinforcement Learning with Self-Distilled Reward Shaping
Ranxu Zhang, Guinan Chen, Chenshaodong +5
Agentic reinforcement learning enables LLM agents to learn through interaction, but sparse trajectory-level rewards reveal success without identifying which intermediate decisions…
cs.LG2024
UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches
Chao Wang, Neo Wu, Lin Ning +5
Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user info…
cs.CL2024
RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs
Jiaxing Wu, Lin Ning, Luyang Liu +7
LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users' behavior from their past activities. However, their effectiveness often hinges on th…