21 citations · 21 across the 7 of their papers we have counts for
8 papers
Mosaic: Multi-Session Agentic Reinforcement Learning
Yilong Xu, Minrui Xu, Xinyu Zhou +3
Most existing agentic reinforcement learning methods assume single-session trajectories, where interaction proceeds within one continuously accumulated context. However, agentic te…
To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models
Haoqing Wang, Xiang Long, Ziheng Li +3
Reinforcement Learning with Verifiable Rewards (RLVR) plays a key role in stimulating the explicit reasoning capability of Large Language Models (LLMs). We can achieve expert-level…
Self-Manager: Parallel Agent Loop for Long-form Deep Research
Yilong Xu, Zhi Zheng, Xiang Long +2
Long-form deep research requires multi-faceted investigations over extended horizons to get a comprehensive report. When handling such complex tasks, existing agents manage context…
An Efficient Rubric-based Generative Verifier for Search-Augmented LLMs
Linyue Ma, Yilong Xu, Xiang Long +1
Search augmentation empowers Large Language Models with retrieval capabilities to overcome the limitations imposed by static parameters. Recently, Reinforcement Learning leverages…
APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation
Yuzhen Zhou, Jiajun Li, Yusheng Su +15
Reinforcement learning (RL) has become a cornerstone in advancing large-scale pre-trained language models (LLMs). Successive generations, including GPT-o series, DeepSeek-R1, Kimi-…
RAVine: Reality-Aligned Evaluation for Agentic Search
Yilong Xu, Xiang Long, Zhi Zheng +1
Agentic search, as a more autonomous and adaptive paradigm of retrieval augmentation, is driving the evolution of intelligent search systems. However, existing evaluation framework…
MiniCPM4: Ultra-Efficient LLMs on End Devices
MiniCPM Team, Chaojun Xiao, Yuxuan Li +80
This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in…
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
Shengding Hu, Yuge Tu, Xu Han +22
The burgeoning interest in developing Large Language Models (LLMs) with up to trillion parameters has been met with concerns regarding resource efficiency and practical expense, pa…