Dense Progress Rewards for Long-Horizon Agents: How Tev Turns Multi-Day Tasks Into Trainable Checkpoints
Open-source agents fail on long-horizon tasks because they lack two things: training data with verifiable intermediate states, and a reward signal that guides multi-hour execution without leaking the final goal. Tev-Gym and its progress-potential reward shaping (PPRS) recipe address both. This is i…
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- 2026-10-14 10:05 · DEV Community — Machine Learning
Dense Progress Rewards for Long-Horizon Agents: How Tev Turns Multi-Day Tasks Into Trainable Checkpoints