4 papers
Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL
Chenlu Ye, Xuanchang Zhang, Yifan Hao +6
Off-policy problems such as policy staleness and training--inference mismatch have become a major bottleneck for training stability and further exploration in LLM RL. The distribut…
Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection
Hantao Zheng, Ning Han, Yawen Zeng +1
Recent weakly supervised video anomaly detection methods have achieved significant advances by employing unified frameworks for joint optimization. However, this paradigm is limite…
Mitigating Estimation Bias with Representation Learning in TD Error-Driven Regularization
Haohui Chen, Zhiyong Chen, Aoxiang Liu +1
Deterministic policy gradient algorithms for continuous control suffer from value estimation biases that degrade performance. While double critics reduce such biases, the explorati…
Mildly Conservative Regularized Evaluation for Offline Reinforcement Learning
Haohui Chen, Zhiyong Chen
Offline reinforcement learning (RL) seeks to learn optimal policies from static datasets without further environment interaction. A key challenge is the distribution shift between…