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terminal agents

topic1 events
papersSEP 12 04:00 UTC

T1: 122B Mixture-of-Experts Model Trained with RL for Terminal Agent Tasks

Researchers released T1, a 122-billion-parameter Mixture-of-Experts model trained via reinforcement learning to act as an agent in terminal environments. The work targets long-horizon workloads such as software development and scientific research, where sustained multi-step command-line use matters. It is presented as part of a broader shift in agent design away from short, single-turn interactions.

agents/reinforcement-learning/mixture-of-experts/long-horizon