mlsanityData to 7 Oct 2026

Ray: A Distributed Framework for Emerging AI Applications

arXiv 1712.05889 ↗First posted 16 Dec 2017cs.DC cs.AI cs.LGcited by 516

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Cited by 41 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray---a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.

Key facts. Cited by 516 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 52; 2022: 75; 2023: 76; 2024: 79; 2025: 119; 2026: 114.

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