Tev-Gym
An infrastructure and task environment collection for training long-horizon agents. It builds verifiable intermediate-state task environments using a three-layer approach consisting of latent world specifications, progressive rendering, and progress metric extraction.
Timeline (last 90 days)
Tev proposed Tev-Gym, reportedly the first long-horizon agent training dataset with verifiable, dense, progressive rewards, covering 7 domains and a total of 1170 validated task environments.
Tev simultaneously provides the data infrastructure (Tev-Gym), the training method (PPRS), and the validation results (Tev-27B), forming a complete closed loop.
Tev-Gym used a latent-world-first approach to build 1,170 validated task environments, each containing a complete hidden model of the task domain, workflow and expected states.
Tev-Gym uses progressive rendering to generate environments from the latent world specification, so complexity is controllable and every intermediate state is verifiable.
All Facts (4)
Tev simultaneously provides the data infrastructure (Tev-Gym), the training method (PPRS), and the validation results (Tev-27B), forming a complete closed loop.
50%UnverifiedTev-Gym used a latent-world-first approach to build 1,170 validated task environments, each containing a complete hidden model of the task domain, workflow and expected states.
50%UnverifiedTev-Gym uses progressive rendering to generate environments from the latent world specification, so complexity is controllable and every intermediate state is verifiable.
50%UnverifiedTev proposed Tev-Gym, reportedly the first long-horizon agent training dataset with verifiable, dense, progressive rewards, covering 7 domains and a total of 1170 validated task environments.
50%