5 papers
Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL
Tong Zheng, Skylar Zhai, Zhan Cheng +7
Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously. Reward-wise normalization, as used in GDPO, preserves rew…
SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
Tianming Sha, Yue Zhao, Lichao Sun +1
Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but corre…
Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?
Wanyi Chen, Xiao Yang, Xu Yang +7
We introduce Agent2 RL-Bench, a compact diagnostic benchmark for evaluating agentic RL post-training, which tests whether LLM agents can autonomously design, implement, debug, and…
Mitigating Cache Noise in Test-Time Adaptation for Large Vision-Language Models
Haotian Zhai, Xinyu Chen, Can Zhang +2
Test-time adaptation (TTA) of visual language models has recently attracted significant attention as a solution to the performance degradation caused by distribution shifts in down…
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
Zhan Cheng, Bolin Shen, Tianming Sha +3
Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GMLaaS) platforms, yet they remain vulnerable to graph-based model extraction attack…