Long Tasks Gain a Progress Meter

Long-horizon agent tasks can supply a final pass or fail signal while leaving thousands of intermediate choices unexplained. Tev-Gym begins with a hidden specification of the domain, workflow and expected state, then renders 1,170 environments across seven domains. That latent specification also yields a progress measure, which the authors turn into per-step reward shaping. They report gains for a 27-billion-parameter model on computing, math and terminal benchmarks. Comparisons to named frontier models are claims from the paper's evaluation and depend on its task subsets and Pass@5 protocol. The durable contribution is the training structure: verified progress can make sparse, multi-hour work more learnable.