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cs.LG2026
Enhancing Bandit Algorithms with LLMs for Time-varying User Preferences in Streaming Recommendations
Chenglei Shen, Yi Zhan, Weijie Yu +2
In real-world streaming recommender systems, user preferences evolve dynamically over time. Existing bandit-based methods treat time merely as a timestamp, neglecting its explicit…
cs.LG2025
Beyond High-Entropy Exploration: Correctness-Aware Low-Entropy Segment-Based Advantage Shaping for Reasoning LLMs
Xinzhu Chen, Xuesheng Li, Zhongxiang Sun +1
Reinforcement Learning with Verifiable Rewards (RLVR) has become a central approach for improving the reasoning ability of large language models. Recent work studies RLVR through t…