Adobe & UCLA's Tev: verifiable progress rewards lift long-horizon agent Pass@5 from 0.17 to 0.28
jiqizhixin · x · 2026-11-02
Adobe Research and UCLA introduce Tev, tackling long-horizon agent tasks where almost every early attempt fails and feedback before the final answer is scarce, by combining synthetic environments with verifiable progress rewards.
- Build the answer world first: Tev-Gym contains 1,170 environments across seven domains, each starting from a hidden specification that renders input files, controlled defects, and a workflow graph, so tasks and validators share the same underlying state
- Reward measurable progress: a progress contract checks intermediate work against that hidden world; Progress-Potential Reward Shaping distinguishes partially successful rollouts even when none passes the final verifier, giving group-relative training a usable signal
- Explicit benchmark scope: on 18 ALE-CM tasks, Tev-27B improves Pass@5 from 0.17 to 0.28, matching reported GPT-6
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