25 citations · 34 across the 6 of their papers we have counts for
7 papers
JURY-RL: Votes Propose, Proofs Dispose for Label-Free RLVR
Xinjie Chen, Biao Fu, Jing Wu +4
Reinforcement learning with verifiable rewards (RLVR) enhances the reasoning of large language models (LLMs), but standard RLVR often depends on human-annotated answers or carefull…
CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks
Hao Wang, Licheng Pan, Zhichao Chen +7
Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models, current reward modeling heavily relies on experimental feedback data collected…
From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization
Xinjie Chen, Minpeng Liao, Guoxin Chen +4
Reinforcement learning with verifiable rewards (RLVR) has recently advanced the reasoning capabilities of large language models (LLMs). While prior work has emphasized algorithmic…
FreDF: Learning to Forecast in the Frequency Domain
Hao Wang, Licheng Pan, Zhichao Chen +6
Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research predominantly addresses autocorrelation…
Modeling Task Relationships in Multi-variate Soft Sensor with Balanced Mixture-of-Experts
Yuxin Huang, Hao Wang, Zhaoran Liu +3
Accurate estimation of multiple quality variables is critical for building industrial soft sensor models, which have long been confronted with data efficiency and negative transfer…
TMoE-P: Towards the Pareto Optimum for Multivariate Soft Sensors
Licheng Pan, Hao Wang, Zhichao Chen +2
Multi-variate soft sensor seeks accurate estimation of multiple quality variables using measurable process variables, which have emerged as a key factor in improving the quality of…
Entire Space Counterfactual Learning for Reliable Content Recommendations
Hao Wang, Zhichao Chen, Zhaoran Liu +4
Post-click conversion rate (CVR) estimation is a fundamental task in developing effective recommender systems, yet it faces challenges from data sparsity and sample selection bias.…