From the 2 of 53 linked papers with an AI index.
6 papers · 1 filter
CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents
Bowen Wang, Dunjie Lu, Junli Wang +11
Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents…
WebWorld: A Large-Scale World Model for Web Agent Training
Zikai Xiao, Jianhong Tu, Chuhang Zou +7
Web agents require massive trajectories to generalize, yet real-world training is constrained by network latency, rate limits, and safety risks. We introduce \textbf{WebWorld} seri…
DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints
Yinger Zhang, Shutong Jiang, Renhao Li +6
While agent evaluation has shifted toward long-horizon tasks, most benchmarks still emphasize local, step-level reasoning rather than the global constrained optimization (e.g., tim…
ToolRM: Towards Agentic Tool-Use Reward Modeling
Renhao Li, Jianhong Tu, Yang Su +6
Reward models (RMs) play a critical role in aligning large language models (LLMs) with human preferences. Yet in the domain of tool learning, the lack of RMs specifically designed…
ProcessBench: Identifying Process Errors in Mathematical Reasoning
Chujie Zheng, Zhenru Zhang, Beichen Zhang +6
As language models regularly make mistakes when solving math problems, automated identification of errors in the reasoning process becomes increasingly significant for their scalab…
MARGE: Improving Math Reasoning for LLMs with Guided Exploration
Jingyue Gao, Runji Lin, Keming Lu +3
Large Language Models (LLMs) exhibit strong potential in mathematical reasoning, yet their effectiveness is often limited by a shortage of high-quality queries. This limitation nec…