🔥 Tmax: A simple recipe for terminal agents

💡 The paper presents a novel approach to training terminal agents using reinforcement learning, called Tmax. Terminal agents are a popular application of language models, but their training has been hindered by the lack of simple and effective methods, limited data, and challenging benchmarks. The authors address these issues by introducing a simplified recipe for training terminal agents, which achieves superior performance with fewer parameters than previous methods.

The method involves generating a large dataset of terminal environments using a novel taxonomy that combines difficulty control, personas, and verifier diversification. This allows for the cheap generation of large amounts of data, which is then used to train open-weight models using reinforcement learning with a simple outcome-only recipe.

The results show that Tmax achieves 27 percent on Terminal-Bench 2.0 with only 9 billion parameters, outperforming much larger models from prior work. The authors also release their terminal dataset, which is over 2.5 times larger than previously released terminal-agent datasets, as well as their models and code as a strong baseline for future academic work on terminal agents.

The contributions of the paper are threefold. First, it presents a simple and effective recipe for training terminal agents, which can be used as a baseline for future work. Second, it introduces a novel taxonomy for generating terminal environments, which allows for the cheap generation of large amounts of data. Third, it releases a large dataset of terminal environments, models, and code, which can be used by other researchers to advance the field of terminal agents. Overall, the paper provides a significant contribution to the field of terminal agents and reinforcement learning, and has the potential to advance the state of the art in this area.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.23321
• PDF: https://arxiv.org/pdf/2606.23321
• Project Page: https://wai-org.com/blog/tmax/

🤖 Models citing this paper:
• https://huggingface.co/allenai/tmax-27b
• https://huggingface.co/allenai/tmax-9b
• https://huggingface.co/allenai/qwen35-9b-openthoughts

📊 Datasets citing this paper:
• https://huggingface.co/datasets/allenai/TMax-15K
• https://huggingface.co/datasets/allenai/tmax-15k-open-instruct
• https://huggingface.co/datasets/allenai/tmax-sft

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📢 By: https://xn--r1a.website/PaperNexus

#TerminalAgents #ReinforcementLearning #LanguageModels #TmaxAlgorithm #AgentTrainingMethods