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    Meet SETA: Open Supply Coaching Reinforcement Studying Environments for Terminal Brokers with 400 Duties and CAMEL Toolkit

    Naveed AhmadBy Naveed Ahmad11/01/2026Updated:04/02/2026No Comments3 Mins Read
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    **What’s the Big Deal About SETA for Terminal Brokers?**

    I recently stumbled upon some exciting news in the world of AI – a new toolkit and environment stack called SETA (Scalable Environments for Terminal Agents) has been launched. SETA is all about reinforcement learning for terminal brokers, which are agents that operate within a Unix shell and can complete verifiable tasks under a benchmark harness like Terminal Bench.

    So, what’s the big deal? Well, SETA has made three significant contributions that can benefit terminal brokers and AI enthusiasts alike.

    **Cutting-Edge Terminal Agents**

    The SETA team has achieved impressive efficiency with a Claude Sonnet 4.5-based agent on Terminal Bench 2.0 and a GPT 4.1-based agent on Terminal Bench 1.0. These agents are a testament to the power of AI in processing terminal tasks.

    **Scalable RL Training with Artificial Terminal Environments**

    The SETA team has created an initial dataset with 400 terminal tasks, covering a range of difficulty levels. They used 260 of these tasks to fine-tune a Qwen3-8B model. This dataset is a valuable resource for anyone looking to train and evaluate terminal agents.

    **Clear Agent Design**

    The SETA agents are designed to generalize across training and evaluation frameworks, making it easier to debug and evaluate terminal brokers. The same agent implementation can be used for both local process runs and the official Terminal Bench evaluation harness.

    **The SETA Code Repository**

    The SETA code repository is a treasure trove of useful tools and resources. The Terminal Toolkit transforms a language model into an executable terminal agent, and the log structure is designed to make it easy to track and analyze agent performance.

    **Key Takeaways**

    SETA is a joint community effort that provides agent toolkits and artificial RL environments specifically for terminal brokers. The framework has achieved impressive efficiency with cutting-edge agents, and the dataset is a valuable resource for anyone looking to train and evaluate terminal agents.

    **Try SETA Today!**

    If you’re interested in trying out SETA, you can access the code repository and technical details on the SETA GitHub page. You can also follow the SETA team on Twitter and join the 100k+ ML SubReddit to stay up-to-date on the latest developments.

    **Special Thanks**

    I’d like to extend a special thank you to Michal Sutter for his contributions to this blog post. His insights and expertise have been invaluable in helping me understand the significance of SETA for terminal brokers.

    **Stay Tuned!**

    Keep an eye on ai2025.dev, a 2025-focused analytics platform that turns model launches, benchmarks, and ecosystem activity into a structured dataset you can filter, compare, and export. And, don’t forget to subscribe to our newsletter and follow us on Twitter for the latest updates!

    Naveed Ahmad

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