collaborators

8 papers

cs.AI2026

GDEPO: Group Dual-dynamic and Equal-right Advantage Policy Optimization for Sample-Efficient Reinforcement Learning

Zhengqing Yan, Xinyang Liu, Yi Zhang +7

Automated Theorem Proving (ATP) is a fundamental AI challenge, requiring machine-verifiable proofs in formal languages like Lean to evaluate reasoning. Reinforcement Learning (RL),…

cs.MA2025

DISPATCH -- Decentralized Informed Spatial Planning and Assignment of Tasks for Cooperative Heterogeneous Agents

Yao Liu, Sampad Mohanty, Elizabeth Ondula +1

Spatial task allocation in systems such as multi-robot delivery or ride-sharing requires balancing efficiency with fair service across tasks. Greedy assignment policies that match…

cs.LG2025

Ask a Strong LLM Judge when Your Reward Model is Uncertain

Zhenghao Xu, Qin Lu, Qingru Zhang +9

Reward model (RM) plays a pivotal role in reinforcement learning with human feedback (RLHF) for aligning large language models (LLMs). However, classical RMs trained on human prefe…

cs.CL2025

Optimal Aggregation of LLM and PRM Signals for Efficient Test-Time Scaling

Peng Kuang, Yanli Wang, Xiaoyu Han +3

Process reward models (PRMs) are a cornerstone of test-time scaling (TTS), designed to verify and select the best responses from large language models (LLMs). However, this promise…

cs.LG2025

Improving Sampling Efficiency in RLVR through Adaptive Rollout and Response Reuse

Yuheng Zhang, Wenlin Yao, Changlong Yu +5

Large language models (LLMs) have achieved impressive reasoning performance, with reinforcement learning with verifiable rewards (RLVR) emerging as a standard paradigm for post-tra…

cs.CL2025

The Bias is in the Details: An Assessment of Cognitive Bias in LLMs

R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3

As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…

cs.LG2025

GUIDE: Towards Scalable Advising for Research Ideas

Yaowenqi Liu, Bingxu Meng, Rui Pan +4

The field of AI research is advancing at an unprecedented pace, enabling automated hypothesis generation and experimental design across diverse domains such as biology, mathematics…

cs.IR2025

Towards Large-scale Generative Ranking

Yanhua Huang, Yuqi Chen, Xiong Cao +17

Generative recommendation has recently emerged as a promising paradigm in information retrieval. However, generative ranking systems are still understudied, particularly with respe…