1 citations · 2 across the 12 of their papers we have counts for
6 papers · 1 filter
BLADE: Boundary-Expanded and Layer-Adaptive Dynamic Exit for Efficient LLM Reasoning
Keshu Fu, Keqin Peng, Jun Bai +6
Large language models often improve task performance by generating long reasoning traces, but the resulting computation is frequently wasted on redundant verification and revision.…
RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling
Yang Liu, Jiaqi Li, Zilong Zheng
Rule-based reasoning is acknowledged as one of the fundamental problems of reasoning. While recent studies show that large reasoning models (LRMs) have remarkable reasoning capabil…
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…
LIFT: Improving Long Context Understanding Through Long Input Fine-Tuning
Yansheng Mao, Jiaqi Li, Fanxu Meng +3
Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine-Tuning (LIFT) for long context…
In-Context Editing: Learning Knowledge from Self-Induced Distributions
Siyuan Qi, Bangcheng Yang, Kailin Jiang +5
In scenarios where language models must incorporate new information efficiently without extensive retraining, traditional fine-tuning methods are prone to overfitting, degraded gen…
LooGLE: Can Long-Context Language Models Understand Long Contexts?
Jiaqi Li, Mengmeng Wang, Zilong Zheng +1
Large language models (LLMs), despite their impressive performance in various language tasks, are typically limited to processing texts within context-window size. This limitation…