31 citations · 50 across the 51 of their papers we have counts for
6 papers · 1 filter
AdaStep: Adaptive Step Credit Weighting for Agentic Reinforcement Learning
Xin Wang, Wenhao Wu, Menghao Zhang +3
Long-horizon LLM agents are typically trained with sparse outcome rewards, making trajectory-level objectives too coarse to distinguish the contribution of individual decisions. St…
Learning Reliable GUI Agents under Imperfect Priors
Bo Han, Qianyi Wang, Shuai Liu +9
GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GUI tasks depends on app-specif…
SEE: Structure-aware Exploring & Exploiting for Long-horizon GUI Agent Trajectory Synthesis
Zhuohang Fan, Beichen Zhang, Yuanfa Li +4
Graphical User Interface (GUI) agents powered by vision-language models hold promise for automating real-world mobile tasks. However, progress is limited by the lack of high-covera…
R^3: Replay, Reflection, and Ranking Rewards for LLM Reinforcement Learning
Zhizheng Jiang, Kang Zhao, Weikai Xu +5
Large reasoning models (LRMs) aim to solve diverse and complex problems through structured reasoning. Recent advances in group-based policy optimization methods have shown promise…
End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning
Guanzhong Chen, Shaoxiong Yang, Chao Li +3
Large language models (LLMs) are versatile, yet their deployment in complex real-world settings is limited by static knowledge cutoffs and the difficulty of producing controllable…
Mixture of Diverse Size Experts
Manxi Sun, Wei Liu, Jian Luan +2
The Sparsely-Activated Mixture-of-Experts (MoE) has gained increasing popularity for scaling up large language models (LLMs) without exploding computational costs. Despite its succ…