9 papers
WebAnything: From the Wild Web to Scalable Agent Environments
Tianci Xie, Zikun Cai, Chaoyun Zhang +4
Web agents require diverse, executable, and resettable website environments for reproducible training and evaluation. Human-assisted mirrors demand per-site engineering, while gene…
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution
Hang He, Linting Wang, Guanming Liu +14
Scaling interactive environments is important for training long-horizon agents through reinforcement learning. These environments often require further testing and refinement after…
Work2Skill: Capturing Human GUI Experience for Reusable Agent Skill Induction
Yuqi Huang, Hui Li, Yifei Cao +9
Computer-use agents can perform increasingly complex GUI tasks, yet unfamiliar software and workflows requiring human domain knowledge remain challenging. Human demonstrations conv…
WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents
Bo Mao, Hang He, Linting Wang +17
Recent efforts to scale tool-use post-training have largely centered on the synthesis of executable environments, which constitute only one component of a broader agentic interacti…
NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMs
Jiarong Zhao, Zhikai Lei, Zhiheng Xi +5
Scaling executable agent training data for LLM post-training is bottlenecked by substrate-bound methods that tie task generation to predefined tools, repositories, or skill graphs:…
Steering LLMs via Scalable Interactive Oversight
Enyu Zhou, Zhiheng Xi, Long Ma +9
As Large Language Models increasingly automate complex, long-horizon tasks such as \emph{vibe coding}, a supervision gap has emerged. While models excel at execution, users often s…
ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development
Jie Yang, Honglin Guo, Li Ji +11
The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-…
Toward Optimal LLM Alignments Using Two-Player Games
Rui Zheng, Hongyi Guo, Zhihan Liu +10
The standard Reinforcement Learning from Human Feedback (RLHF) framework primarily focuses on optimizing the performance of large language models using pre-collected prompts. Howev…
Improving Reinforcement Learning from Human Feedback Using Contrastive Rewards
Wei Shen, Xiaoying Zhang, Yuanshun Yao +3
Reinforcement learning from human feedback (RLHF) is the mainstream paradigm used to align large language models (LLMs) with human preferences. Yet existing RLHF heavily relies on…