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cs.LG2026
daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently
Mohan Jiang, Dayuan Fu, Junhao Shi +8
While Large Language Models (LLMs) excel at short-term tasks, scaling them to long-horizon agentic workflows remains challenging. The core bottleneck lies in the scarcity of traini…
cs.LG2026
IRPM: Intergroup Relative Preference Modeling for Pointwise Generative Reward Models
Haonan Song, Qingchen Xie, Huan Zhu +12
Generative Reward Models (GRMs) have demonstrated strong performance in reward modeling, due to their interpretability and potential for refinement through reinforcement learning (…
cs.LG2025
Reward Shaping to Mitigate Reward Hacking in RLHF
Jiayi Fu, Xuandong Zhao, Chengyuan Yao +4
Reinforcement learning from human feedback (RLHF) is widely used to align large language models (LLMs) with human preferences. However, RLHF remains vulnerable to \emph{reward hack…