85 citations · 213 across the 76 of their papers we have counts for
11 papers · 1 filter
SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
Jinyang Wu, Shuo Yang, Zhengxi Lu +8
Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based rein…
OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning
Shuo Yang, Jinyang Wu, Zhengxi Lu +8
Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little guidance on which intermedi…
Exploring Knowledge Purification in Multi-Teacher Knowledge Distillation for LLMs
Ruihan Jin, Pengpeng Shao, Zhengqi Wen +5
Knowledge distillation has emerged as a pivotal technique for transferring knowledge from stronger large language models (LLMs) to smaller, more efficient models. However, traditio…
Atlas: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning
Jinyang Wu, Guocheng Zhai, Ruihan Jin +5
The integration of large language models (LLMs) with external tools has significantly expanded the capabilities of AI agents. However, as the diversity of both LLMs and tools incre…
Deep Learning Approaches for Multimodal Intent Recognition: A Survey
Jingwei Zhao, Yuhua Wen, Qifei Li +8
Intent recognition aims to identify users' underlying intentions, traditionally focusing on text in natural language processing. With growing demands for natural human-computer int…
Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM Reasoning
Kaiying Yan, Moyang Liu, Yukun Liu +4
The rapid spread of fake news across multimedia platforms presents serious challenges to information credibility. In this paper, we propose a Debunk-and-Infer framework for Fake Ne…