Tev

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   Tev-27B: Long-Horizon Agent Training with Dense Progress Rewards

   An arXiv paper introduces Tev-Gym, described as the first long-horizon agent training dataset with verifiable, dense, progressive rewards, spanning 1,170 verified task environments across 7 domains. Using a latent-world-first approach, the authors first build a complete hidden specification of each task domain, workflow and expected per-step state, then progressively render it into an agent-facing environment, and convert progress into dense per-step rewards via progress-potential reward shaping (PPRS) to train Tev-27B. The paper reports that Tev-27B matches GPT-6 Astra and Claude Opus 5 at Pass@5 on the Agents' Last Exam computing and math sciences domains (ALE-CM), and that despite training only on ALE-related data it generalizes to Terminal-Bench 3.0 and 4.0 subsets, surpassing GLM 5.2 and Gemini 3.7 Flash and reaching Claude Opus 4.8-level performance on mid-horizon tasks such as TB 2.1.

   Hugging Face · Papers · 🔥 0

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