10 citations · 15 across the 10 of their papers we have counts for
3 papers · 1 filter
ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning
Yuchen Zeng, Shuibai Zhang, Wonjun Kang +9
Large Reasoning Models (LRMs) are Large Language Models (LLMs) explicitly trained to generate long-form Chain-of-Thoughts (CoTs), achieving impressive success on challenging tasks…
Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges
Nayoung Lee, Ziyang Cai, Avi Schwarzschild +2
Large language models often struggle with length generalization and solving complex problem instances beyond their training distribution. We present a self-improvement approach whe…
Task Vectors in In-Context Learning: Emergence, Formation, and Benefit
Liu Yang, Ziqian Lin, Kangwook Lee +2
In-context learning is a remarkable capability of transformers, referring to their ability to adapt to specific tasks based on a short history or context. Previous research has fou…