Echoverse: Deep, evolving environments for computer-use agents

Scaling fidelity over sheer count, targeting the capabilities agents actually lack, and evolving with the models they train. We built twelve training worlds for computer-use agents: ten deep domain worlds and two capability worlds, each drilling a single control rendered in many forms (date pickers and nested filters). Depth is what makes them worth training on: these worlds reproduce an application’s real behavior, come seeded with realistic data, and keep state coherent across screens and users. Trained on all twelve, a 9B model nearly doubles its base score (36.5% to 67.1%), coming within fourteen points of GPT-5.4. The experiment taught us several lessons: High simulation fidelity is a must-have; shallow worlds hurt the agent. Trained on shallow and deep builds of the same sites, the model regressed on the shallow ones but improved on the deep ones. Agents often struggle with the same challenging UI elements, like date pickers and nested filters. Drilling those controls in varied forms taught the model to operate them in domains it never saw in training. Co-evolving the model, the world, and the verifier improves all of them. As the world grows more correct and its tasks grow harder, the model climbs with it. Reinforcement…