The Abstraction and Reasoning Corpus (ARC) tests AI systems' ability to perform human-like inductive reasoning from a few demonstration pairs. Existing Gymnasium-based RL environments severely limit experimental scale due to computational bottlenecks. We present JaxARC, an open-source, high-performance RL environment for ARC implemented in JAX. Its functional, stateless architecture enables massive parallelism, achieving 38-5,439x speedup over Gymnasium at matched batch sizes, with peak throughput of 790M steps/second. JaxARC supports multiple ARC datasets, flexible action spaces, composable wrappers, and configuration-driven reproducibility, enabling large-scale RL research previously computationally infeasible. JaxARC is available at https://github.com/aadimator/JaxARC.
@article{arxiv.2601.17564,
title = {JaxARC: A High-Performance JAX-based Environment for Abstraction and Reasoning Research},
author = {Aadam and Monu Verma and Mohamed Abdel-Mottaleb},
journal= {arXiv preprint arXiv:2601.17564},
year = {2026}
}