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cs.LG2026

Unlocking In-Context Learning in Audio-Language Models from Decentralized Medical Audio

Ran Piao, Tsai-Ning Wang, Martijn den Dekker +4

Clinical audio diagnosis in low-resource settings requires models that identify conditions from minimal examples without large annotated corpora. We propose Federated Self-Contextu…

cs.LG2026

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…

cs.LG2026

Optimal Transport for LLM Reward Modeling from Noisy Preference

Licheng Pan, Haochen Yang, Haoxuan Li +8

Reward models are fundamental to Reinforcement Learning from Human Feedback (RLHF), yet real-world datasets are inevitably corrupted by noisy preference. Conventional training obje…

cs.LG2026

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…

cs.LG2026

Rewards as Labels: Revisiting RLVR from a Classification Perspective

Zepeng Zhai, Meilin Chen, Jiaxuan Zhao +3

Reinforcement Learning with Verifiable Rewards has recently advanced the capabilities of Large Language Models in complex reasoning tasks by providing explicit rule-based supervisi…