5 papers
More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
Linhao Zhong, Bozhen Fang, Xuehao Wang +7
Self-distillation in agentic reinforcement learning has produced a growing number of methods. However, existing evaluations often reuse published baseline scores under differing ex…
ForeDreamer: A Self-Evolving Dual-Agent Memory Architecture for Future Event Prediction
Linhao Zhong, Zongze Du, Linyu Wu +6
Open-web future event prediction requires agents to distill reliable signals from noisy, redundant, and incomplete evidence. Existing retrieval/memory mechanisms directly feed retr…
Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration
Linhao Zhong, Linyu Wu, Wen Wang +5
Diffusion large language models (dLLMs) have recently attracted significant attention for their ability to enhance diversity, controllability, and parallelism. However, their non-s…
User-Friendly Customized Generation with Multi-Modal Prompts
Linhao Zhong, Yan Hong, Wentao Chen +4
Text-to-image generation models have seen considerable advancement, catering to the increasing interest in personalized image creation. Current customization techniques often neces…
Improve Cross-Architecture Generalization on Dataset Distillation
Binglin Zhou, Linhao Zhong, Wentao Chen
Dataset distillation, a pragmatic approach in machine learning, aims to create a smaller synthetic dataset from a larger existing dataset. However, existing distillation methods pr…