3 citations · 4 across the 14 of their papers we have counts for
4 papers · 1 filter
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation
Yongcan Yu, Jian Liang, Lingxiao He +5
While long chain-of-thought (CoT) distillation significantly improves the reasoning abilities of language models, its underlying mechanism remains poorly understood. Current pipeli…
When Not to Imitate: Boundary-Aware Skill Memory for Reliable Tool-Use LLM Agents
Zihan Lin, Zhenyu Chen, Jiawen Wei +6
Extracting skills from past successes is critical for the efficient evolution of Large Language Model (LLM) agents. Prevailing agent self-evolution paradigms typically rely on a co…
AWPO: Enhancing Tool-Use of Large Language Models through Adaptive Integration of Reasoning Rewards
Zihan Lin, Xiaohan Wang, Hexiong Yang +5
While Reinforcement Learning (RL) shows promise in training tool-use Large Language Models (LLMs) using verifiable outcome rewards, existing methods largely overlook the potential…
ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Models
Zihan Lin, Xiaohan Wang, Jie Cao +4
Large language models (LLMs) transcend passive generation and act as goal-directed agents by invoking external tools. Reinforcement learning (RL) offers a principled framework for…