3 papers
cs.LG2026
Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation
Claire Chen, Shuze Daniel Liu, Licheng Luo +3
In reinforcement learning policy evaluation, classic on-policy methods often suffer from high variance when estimating policy performance. To mitigate this issue, behavior policy s…
cs.LG2026
Beyond Semantic Manipulation: Token-Space Attacks on Reward Models
Yuheng Zhang, Mingyue Huo, Minghao Zhu +2
Reward models (RMs) are widely used as optimization targets in reinforcement learning from human feedback (RLHF), yet they remain vulnerable to reward hacking. Existing attacks mai…
cs.LG2025
Statistical Tractability of Off-policy Evaluation of History-dependent Policies in POMDPs
Yuheng Zhang, Nan Jiang
We investigate off-policy evaluation (OPE), a central and fundamental problem in reinforcement learning (RL), in the challenging setting of Partially Observable Markov Decision Pro…