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[2601.16443] Endless Terminals: Scaling RL Environments for Terminal Agents
Summary
The paper presents 'Endless Terminals', a scalable reinforcement learning (RL) environment designed for training terminal agents through a fully autonomous pipeline that generates diverse tasks without human intervention.
Why It Matters
This research addresses a critical bottleneck in RL by providing a scalable and efficient method for generating training environments, which can significantly enhance the performance of AI agents. The findings suggest that simpler RL approaches can yield substantial improvements when environments are effectively scaled, making this relevant for AI development and research.
Key Takeaways
- Endless Terminals autonomously generates diverse terminal tasks for RL training.
- The pipeline includes four stages: task description generation, environment validation, completion testing, and solvability filtering.
- Models trained on this pipeline showed significant performance improvements on both generated and human-curated benchmarks.
- Simple RL methods can outperform complex approaches when environments are scaled effectively.
- The research highlights the importance of scalable environments in enhancing agent performance.
Computer Science > Machine Learning arXiv:2601.16443 (cs) [Submitted on 23 Jan 2026 (v1), last revised 14 Feb 2026 (this version, v3)] Title:Endless Terminals: Scaling RL Environments for Terminal Agents Authors:Kanishk Gandhi, Shivam Garg, Noah D. Goodman, Dimitris Papailiopoulos View a PDF of the paper titled Endless Terminals: Scaling RL Environments for Terminal Agents, by Kanishk Gandhi and 3 other authors View PDF HTML (experimental) Abstract:Environments are the bottleneck for self-improving agents. Current terminal benchmarks were built for evaluation, not training; reinforcement learning requires a scalable pipeline, not just a dataset. We introduce Endless Terminals, a fully autonomous pipeline that procedurally generates terminal-use tasks without human annotation. The pipeline has four stages: generating diverse task descriptions, building and validating containerized environments, producing completion tests, and filtering for solvability. From this pipeline we obtain 3255 tasks spanning file operations, log management, data processing, scripting, and database operations. We train agents using vanilla PPO with binary episode level rewards and a minimal interaction loop: no retrieval, multi-agent coordination, or specialized tools. Despite this simplicity, models trained on Endless Terminals show substantial gains: on our held-out dev set, Llama-3.2-3B improves from 4.0% to 18.2%, Qwen2.5-7B from 10.7% to 53.3%, and Qwen3-8B-openthinker-sft from 42.6% to 59.0%. ...
