5 papers
Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence
Haocheng Yang, Licheng Pan, Xiaoxi Li +5
Rubrics provide structured, fine-grained signals for training and evaluating large language models (LLMs). Yet reliable query-specific rubrics are difficult to construct. Existing…
Uncertainty-Aware Reward Modeling for Stable RLHF
Licheng Pan, Haocheng Yang, Haoxuan Li +7
Reinforcement learning from human feedback (RLHF) aligns large language models by training reward models on preference data and optimizing policies to maximize predicted rewards. H…
Looped World Models
Hongyuan Adam Lu, Z. L. Victor Wei, Qun Zhang +28
Current world models face a fundamental tension: faithful long-horizon simulation demands deep computation, but deeper models are expensive to deploy and prone to compounding error…
Mitigating Hidden Confounding by Progressive Confounder Imputation via Large Language Models
Hao Yang, Haoxuan Li, Luyu Chen +3
Hidden confounding remains a central challenge in estimating treatment effects from observational data, as unobserved variables can lead to biased causal estimates. While recent wo…
Estimating the Effects of Sample Training Orders for Large Language Models without Retraining
Hao Yang, Haoxuan Li, Mengyue Yang +2
The order of training samples plays a crucial role in large language models (LLMs), significantly impacting both their external performance and internal learning dynamics. Traditio…