The researchers introduce Endless Terminals, an innovative autonomous pipeline designed to generate a vast array of verifiable tasks for training AI agents in terminal environments. By using a four-stage procedural generation process, the system creates diverse task descriptions, validates containerized setups, produces completion tests, and filters for solvability using advanced frontier models. This method addresses the historical scarcity of high-quality data, which has previously limited the effectiveness of Reinforcement Learning (RL) for command-line agents. Training smaller models on the resulting 3,255 synthetic tasks led to significant performance gains on both a dedicated development set and independent, human-curated benchmarks. The study emphasizes that scaling environments is more critical for agent improvement than increasing algorithmic complexity or using specialized tools. Ultimately, the results suggest that simple RL frameworks can achieve state-of-the-art capabilities when provided with an endless stream of autonomously generated training data. Source: January 2026 Endless Terminals: Scaling RL Environments for Terminal Agents Stanford University, Microsoft Research, UW-Madison Kanishk Gandhi, Shivam Garg, Noah D. Goodman, Dimitris Papailiopoulos https://arxiv.org/pdf/2601.16443