1 citations · 1 across the 7 of their papers we have counts for
7 papers
Can RL Improve Generalization of LLM Agents? An Empirical Study
Zhiheng Xi, Xin Guo, Jiaqi Liu +11
Reinforcement fine-tuning (RFT) has shown promise for training LLM agents to perform multi-turn decision-making based on environment feedback. However, most existing evaluations re…
Counteracting Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing
Xin Guo, Zhiheng Xi, Yiwen Ding +6
Self-improvement has emerged as a mainstream paradigm for advancing the reasoning capabilities of large vision-language models (LVLMs), where models explore and learn from successf…
LongCat-Flash Technical Report
Meituan LongCat Team, Bayan, Bei Li +179
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…
MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use
Weikang Zhao, Xili Wang, Chengdi Ma +6
With the recent rapid advancement of Agentic Intelligence, agentic tool use in LLMs has become increasingly important. During multi-turn interactions between agents and users, the…
MoTE: Mixture of Ternary Experts for Memory-efficient Large Multimodal Models
Hongyu Wang, Jiayu Xu, Ruiping Wang +5
Large multimodal Mixture-of-Experts (MoEs) effectively scale the model size to boost performance while maintaining fixed active parameters. However, previous works primarily utiliz…
Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision
Zhiheng Xi, Dingwen Yang, Jixuan Huang +21
Training large language models (LLMs) to spend more time thinking and reflection before responding is crucial for effectively solving complex reasoning tasks in fields such as scie…