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cs.LG2026
Actor-Curator: Co-adaptive Curriculum Learning via Policy-Improvement Bandits for RL Post-Training
Zhengyao Gu, Jonathan Light, Raul Astudillo +7
Post-training large foundation models with reinforcement learning typically relies on massive and heterogeneous datasets, making effective curriculum learning both critical and cha…
cs.LG2024
Beyond Numeric Rewards: In-Context Dueling Bandits with LLM Agents
Fanzeng Xia, Hao Liu, Yisong Yue +1
In-Context Reinforcement Learning (ICRL) is a frontier paradigm to solve Reinforcement Learning (RL) problems in the foundation model era. While ICRL capabilities have been demonst…
cs.LG2024
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits
Yihong Guo, Hao Liu, Yisong Yue +1
We introduce a distributionally robust approach that enhances the reliability of offline policy evaluation in contextual bandits under general covariate shifts. Our method aims to…