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ARC (Abstraction and Reasoning Corpus) is a dataset jointly created by Gwangju Institute of Science and Technology and Korea University, intended to evaluate and improve AI systems' abstract reasoning capabilities. It contains various complex grid‑editing tasks with large action spaces and diverse task types. The dataset was generated using the Gymnasium environment by defining specific action and state spaces to simulate ARC challenges. ARC is primarily used in reinforcement learning to develop and test AI models capable of solving complex reasoning problems.
