3 papers
cs.LG2026
Exploration vs Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward
Peter Chen, Xiaopeng Li, Ziniu Li +3
This paper examines the exploration-exploitation trade-off in reinforcement learning with verifiable rewards (RLVR), a framework for improving the reasoning of Large Language Model…
cs.HC2025
MapAgent: Trajectory-Constructed Memory-Augmented Planning for Mobile Task Automation
Yi Kong, Dianxi Shi, Guoli Yang +4
The recent advancement of autonomous agents powered by Large Language Models (LLMs) has demonstrated significant potential for automating tasks on mobile devices through graphical…
cs.LG2025
SWSC: Shared Weight for Similar Channel in LLM
Binrui Zeng, Yongtao Tang, Xiaodong Liu +1
Large language models (LLMs) have spurred development in multiple industries. However, the growing number of their parameters brings substantial storage and computing burdens, maki…