arXiv:2606.23321v2 Announce Type: replace Abstract: Terminal-using agents have quickly become a popular and important downstream application of language models (LMs). Despite their prevalence, relatively little academic work has examined RL-based training of these models, due to difficult benchmarks, a lack of data, and a lack of simple baseline recipes. We present TMax, an open RL recipe for terminal agents that brings small open models closer to the frontier. While simple, our recipe achieves 33% on Terminal-Bench 2.0 with only 9B parameters, outperforming much larger models from prior work. Our recipe first involves generating data using a novel taxonomy, combining difficulty control, personas, and verifier diversification, which allows us to cheaply generate large amounts of terminal environments for RL and SFT training. We then train open-weight models using RL with our data, using a simple, outcome-only recipe, with small adjustments to improve stability. Our recipe works for models from 2B to 27B and across model families, dominating the Pareto curve for open terminal-agent performance at smaller sizes. We release our data, models, and code as a strong baseline for future open academic work on terminal agents at https://github.com/hamishivi/tmax.