most citedCooper: Co-Optimizing Policy and Reward Models in Reinforcement Learning for Large Language Models

1 citations · 2 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2025

SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models

Hongxing Li, Dingming Li, Zixuan Wang +7

Spatial reasoning remains a fundamental challenge for Vision-Language Models (VLMs), with current approaches struggling to achieve robust performance despite recent advances. We id…

cs.LG2025

UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning

Zhengxi Lu, Jiabo Ye, Fei Tang +8

Graphical User Interface (GUI) agents have demonstrated remarkable progress in automating complex user interface interactions through reinforcement learning. However, current appro…

cs.AI2025

LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization

Xingyu Wu, Yuchen Yan, Shangke Lyu +7

Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for…

cs.CL20251 cited

Cooper: Co-Optimizing Policy and Reward Models in Reinforcement Learning for Large Language Models

Haitao Hong, Yuchen Yan, Xingyu Wu +5

Large language models (LLMs) have demonstrated remarkable performance in reasoning tasks, where reinforcement learning (RL) serves as a key algorithm for enhancing their reasoning…

cs.AI2025

Hierarchical Budget Policy Optimization for Adaptive Reasoning

Shangke Lyu, Linjuan Wu, Yuchen Yan +7

Large reasoning models achieve remarkable performance through extensive chain-of-thought generation, yet they suffer from a critical inefficiency: applying uniformly extensive reas…

cs.LG2025

GUI-G: Gaussian Reward Modeling for GUI Grounding

Fei Tang, Zhangxuan Gu, Zhengxi Lu +9

Graphical User Interface (GUI) grounding maps natural language instructions to precise interface locations for autonomous interaction. Current reinforcement learning approaches use…