306 citations · 324 across the 8 of their papers we have counts for
4 papers · 1 filter
LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Zecheng Tang, Baibei Ji, Quantong Qiu +4
Reward model (RM) plays a pivotal role in aligning large language model (LLM) with human preferences. As real-world applications increasingly involve long history trajectories, e.g…
SCAN: Self-Denoising Monte Carlo Annotation for Robust Process Reward Learning
Yuyang Ding, Xinyu Shi, Juntao Li +3
Process reward models (PRMs) offer fine-grained, step-level evaluations that facilitate deeper reasoning processes in large language models (LLMs), proving effective in complex tas…
Learning Active Perception via Self-Evolving Preference Optimization for GUI Grounding
Wanfu Wang, Qipeng Huang, Guangquan Xue +2
Vision Language Models (VLMs) have recently achieved significant progress in bridging visual perception and linguistic reasoning. Recently, OpenAI o3 model introduced a zoom-in sea…
Unlocking Recursive Thinking of LLMs: Alignment via Refinement
Haoke Zhang, Xiaobo Liang, Cunxiang Wang +2
The OpenAI o1-series models have demonstrated that leveraging long-form Chain of Thought (CoT) can substantially enhance performance. However, the recursive thinking capabilities o…