1 citations · 1 across the 6 of their papers we have counts for
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LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning
Yansheng Mao, Yufei Xu, Jiaqi Li +5
Long-context understanding remains challenging for LLMs due to limited context windows. This paper introduces Long Input Fine-Tuning (LIFT), a framework that improves the long-cont…
Proof-RM: A Scalable and Generalizable Reward Model for Math Proof
Haotong Yang, Zitong Wang, Shijia Kang +7
While Large Language Models (LLMs) have demonstrated strong math reasoning abilities through Reinforcement Learning with *Verifiable Rewards* (RLVR), many advanced mathematical pro…
LiteToken: Removing Intermediate Merge Residues From BPE Tokenizers
Yike Sun, Haotong Yang, Zhouchen Lin +1
Tokenization is fundamental to how language models represent and process text, yet the behavior of widely used BPE tokenizers has received far less study than model architectures a…
Beyond Single-Task: Robust Multi-Task Length Generalization for LLMs
Yi Hu, Shijia Kang, Haotong Yang +2
Length generalization, the ability to solve problems longer than those seen during training, remains a critical challenge for large language models (LLMs). Previous work modifies p…
Number Cookbook: Number Understanding of Language Models and How to Improve It
Haotong Yang, Yi Hu, Shijia Kang +2
Large language models (LLMs) can solve an increasing number of complex reasoning tasks while making surprising mistakes in basic numerical understanding and processing (such as 9.1…