9 papers
Mechanistic Insights into Functional Sparsity in Multimodal LLMs via CoRe Heads
Ruoxi Sun, Quantong Qiu, Juntao Li +3
While Multimodal Large Language Models (MLLMs) demonstrate remarkable proficiency on complex vision-language tasks, the mechanisms by which they extract query-relevant visual featu…
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…
Revisiting Long-context Modeling from Context Denoising Perspective
Zecheng Tang, Baibei Ji, Juntao Li +3
Long-context models (LCMs) have demonstrated great potential in processing long sequences, facilitating many real-world applications. The success of LCMs can be attributed to their…
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…
Improving Rationality in the Reasoning Process of Language Models through Self-playing Game
Pinzheng Wang, Juntao Li, Zecheng Tang +2
Large language models (LLMs) have demonstrated considerable reasoning abilities in various tasks such as mathematics and coding. However, recent studies indicate that even the best…
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…