338 citations · 367 across the 22 of their papers we have counts for
7 papers · 1 filter
Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills
Siyuan Huang, Pengyu Cheng, Haotian Liu +10
LLM training is shifting from manual design and annotation to interaction-driven self-evolution. However, existing self-evolutionary methods face a fundamental dilemma between task…
On the Role of Reasoning Patterns in the Generalization Discrepancy of Long Chain-of-Thought Supervised Fine-Tuning
Zhaoyi Li, Xiangyu Xi, Zhengyu Chen +6
Supervised Fine-Tuning (SFT) on long Chain-of-Thought (CoT) trajectories has become a pivotal phase in building large reasoning models. However, how CoT trajectories from different…
Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models
Zhaoyi Li, Jiatong Li, Gangwei Jiang +3
Chain-of-thought (CoT) reasoning has become the standard paradigm for enabling Large Language Models (LLMs) to solve complex problems. However, recent studies reveal a sharp perfor…
Learning to Substitute Components for Compositional Generalization
Zhaoyi Li, Gangwei Jiang, Chenwang Wu +3
Despite the rising prevalence of neural language models, recent empirical evidence suggests their deficiency in compositional generalization. One of the current de-facto solutions…
Mitigate Negative Transfer with Similarity Heuristic Lifelong Prompt Tuning
Chenyuan Wu, Gangwei Jiang, Defu Lian
Lifelong prompt tuning has significantly advanced parameter-efficient lifelong learning with its efficiency and minimal storage demands on various tasks. Our empirical studies, how…
Understanding and Patching Compositional Reasoning in LLMs
Zhaoyi Li, Gangwei Jiang, Hong Xie +3
LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. Our research embarks on a quest to uncover the root causes of compositional re…
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt
Gangwei Jiang, Caigao Jiang, Siqiao Xue +4
Continual pre-training has been urgent for adapting a pre-trained model to a multitude of domains and tasks in the fast-evolving world. In practice, a continually pre-trained model…