4 papers · 1 filter
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback
Li Wang, Chen Zhang, Yirong Yang +4
User feedback enables LLMs to refine reusable personalized skills, but often mixes persistent preferences with request-specific corrections. Indiscriminately retaining both risks n…
Learning to Adapt SFT Data for Better Reasoning Generalization
Lisong Sun, Li Wang, Chen Zhang +4
Large language models (LLMs) have achieved remarkable progress, with post-training playing a crucial role in enhancing their reasoning capabilities. Among post-training paradigms,…
TAMTRL: Teacher-Aligned Reward Reshaping for Multi-Turn Reinforcement Learning in Long-Context Compression
Li Wang, Yandong Wang, Xin Yu +3
The rapid progress of large language models (LLMs) has led to remarkable performance gains across a wide range of tasks. However, when handling long documents that exceed the model…
Decoupling Understanding from Reasoning via Problem Space Mapping for Small-Scale Model Reasoning
Li Wang, Changhao Zhang, Zengqi Xiu +4
Despite recent advances in the reasoning capabilities of Large Language Models (LLMs), improving the reasoning ability of Small Language Models (SLMs, e.g., up to 1.5B parameters)…