2 papers
cs.AI2026
Training Needs Trustworthy Worlds: Verified Synthetic Web Environments for Agent Learning
Chenghao Zhang, Canran Xiao, SaiSai Hu +1
Web agents promise to automate complex digital workflows, but their training remains limited by synthetic environments that look plausible while hiding broken links, inconsistent s…
cs.AI2026
From Solver Feedback to Faithful Plans: Multi-Role Reinforcement Learning for Symbolic Planning
Chenghao Zhang, Yikai Mao, Shanqi Liu +3
Reliable planning requires converting natural-language instructions into executable symbolic specifications, yet large language models remain brittle without costly PDDL annotation…