7 papers
Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence
Wanying Ren, Xin Song, Futing Wang +2
Parameter-based knowledge editing updates the internal knowledge of large language models (LLMs) via localized weight modifications and has attracted significant attention. However…
Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
Yafu Li, Runzhe Zhan, Haoran Zhang +25
Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reaching gold-medal-level performa…
Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning
Bowen Ding, Yuhan Chen, Jiayang Lyv +9
Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) dominate the post-training landscape for mathematical reasoning, yet differ fundamentally in their reliance on expert t…
Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model
Bowen Ding, Yuhan Chen, Futing Wang +2
Large Reasoning Models (LRMs) excel at solving complex problems but face an overthinking dilemma. When handling simple tasks, they often produce verbose responses overloaded with t…
Benchmarking and Rethinking Knowledge Editing for Large Language Models
Guoxiu He, Xin Song, Futing Wang +1
Knowledge editing aims to update the embedded knowledge within Large Language Models (LLMs). However, existing approaches, whether through parameter modification or external memory…
Keys to Robust Edits: from Theoretical Insights to Practical Advances
Jianhao Yan, Futing Wang, Yun Luo +2
Large language models (LLMs) struggle with maintaining accurate knowledge due to conflicting/outdated parametric memories. While locate-and-edit methods address this, their relianc…