arXiv 2606.23321 introduces TMax, a "minimal RL" training framework for terminal-use Agents, jointly released by the University of Washington and Ai2. The standout: a 9B model trained with TMax hits 27.2% on the TerminalBench benchmark — comparable to much larger proprietary Agents — and the team open-sources 14,600 training environments, the most extensive terminal-Agent training corpus to date.
The "minimal RL" design: TMax uses a simple PPO variant with three design choices: (1) outcome-only reward (success or failure on the task); (2) small rollout batch (16 trajectories per update); (3) no critic network (REINFORCE-style baseline). The simplicity is the point — the authors argue that "complex RL tricks" are not necessary for terminal Agents; a clean outcome-based PPO is enough.
The training environments: 14,600 Docker-based terminal environments, each containing a code repository with a specific bug or feature request. The Agent must interact with the terminal (ls, cat, grep, git, etc.) to understand the task and produce a fix. The environments span 12 programming languages and 30 application domains.
The result: the 9B TMax model hits 27.2% on TerminalBench, on par with Claude Code (32.1%) and significantly above the open-source SWE-Agent baseline (15.3%). The model is fully open-sourced, including weights, training code, and the 14,600-environment corpus.
The bigger takeaway: "minimal RL" is a counter-trend signal in a year of increasingly complex Agent training. The TMax paper is a reminder that simple, well-designed pipelines can match complex ones — and the open-source release of the 14,600 environments is a significant contribution to the Agent research community. For the industry, this means "Agent training is reproducible" — any team with a few GPUs can replicate the result.