4 papers
Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families
Yifan Niu, Han Xiao, Dongyi Liu +4
On-Policy Distillation (OPD) has become a core technique in the post-training of Large Language Models (LLMs) for transferring knowledge from domain experts to student models. Howe…
RePO: Bridging On-Policy Learning and Off-Policy Knowledge through Rephrasing Policy Optimization
Linxuan Xia, Xiaolong Yang, Yongyuan Chen +4
Aligning large language models (LLMs) on domain-specific data remains a fundamental challenge. Supervised fine-tuning (SFT) offers a straightforward way to inject domain knowledge…
Learning Evolving Tools for Large Language Models
Guoxin Chen, Zhong Zhang, Xin Cong +5
Tool learning enables large language models (LLMs) to interact with external tools and APIs, greatly expanding the application scope of LLMs. However, due to the dynamic nature of…
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
Yaxi Lu, Shenzhi Yang, Cheng Qian +12
Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scena…