Constructing a realistic virtual training environment can require extensive expert labor before a robot starts learning. Pranjal Aggarwal and Sean Welleck propose Gym-Anything, where coding agents build simulations^[1] and an adversarial audit checks whether their physics and tasks satisfy explicit criteria. The selection of physical work draws on US economic activity instead of focusing solely on familiar toy demonstrations.

RoboWorld contains 25 simulations, 175 training environments and 25 held-out test environments. According to the authors, even the strongest evaluated frontier models failed every test task, averaging 0.06 reward. Reward is a numerical simulator score, not a real-world robot success percentage. The paper also explores learning from repeated attempts, privileged teachers that can inspect simulator state, and demonstrations produced by code policies.

Several approaches improved the measured results, but transfer to physical hardware remains unproven. Automatically generated worlds can misrepresent friction, contact dynamics and unexpected events in actual industrial settings.

The engineering value lies in reproducibility rather than a headline number alone. A replication needs the hardware configuration, training data, evaluation protocol, control frequency and stopping conditions. Comparisons should use consistent compute budgets and attempt counts. Rare failures deserve separate analysis rather than disappearing into an average. Until an independent team reproduces the result on another setup, an improvement is evidence within the authors' experimental conditions, not a universal property of the method. Contact, friction and task conditions should be checked against real observations before relying on simulated scores.