12 papers
Interactive Learning for LLM Reasoning
Hehai Lin, Shilei Cao, Sudong Wang +5
Existing multi-agent learning approaches have developed interactive training environments to explicitly promote collaboration among multiple Large Language Models (LLMs), thereby c…
Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL
Sudong Wang, Weiquan Huang, Xiaomin Yu +9
The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiab…
Skill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems
Hehai Lin, Qi Yang, Chengwei Qin
Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks. However, existing methods face a dilemma b…
CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation
Shilei Cao, Ziyang Gong, Hehai Lin +10
In Remote Sensing (RS), Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key approach to activate the generalizable representation ability of foundation models for downstrea…
Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models
Shilei Cao, Hehai Lin, Jiashun Cheng +9
While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse downstream tasks, the escalatin…
The Illusion of Multi-Agent Advantage
Prathyusha Jwalapuram, Hehai Lin, Chuyuan Li +7
Prevailing wisdom posits that Multi-Agent Systems (MAS) are superior to Single-Agent Systems (SAS), citing advantages like context protection, parallel processing and distributed d…