Can web agents learn through imagination? DynaWeb: Model-Based Reinforcement Learning of Web Agents has been accepted to EMNLP 2026 Main Conference. Training web agents with online RL means costly, risky interaction with the live web. DynaWeb learns a Web World Model instead, letting agents dream web interactions and train on imagined rollouts. - Learns naturalistic web transitions from agent actions - Generates imagined trajectories in a synthetic web environment - Interleaves imagined rollouts with real expert trajectories - Consistent gains for open-source agents on WebArena and WebVoyager Imagination as a scalable path toward online agentic RL. This is part of our ongoing work at Gradient on distributed and efficient RL: training capable agents without the cost, latency, and risk of centralizing them. Paper: Code: