Antoine developed ONNX inference support for the pinterest/ray repository, expanding the inference pipeline to handle ONNX models and introducing a command-line argument to enable ONNX-based inference. Using Python, ONNX, and PyTorch, he addressed deployment flexibility and improved inference performance by allowing models to run efficiently across different backends. Antoine also resolved a static-dimension issue in the ONNX export path for Torch attention models, which enhanced stability and compatibility between frameworks. His work included validating the ONNX inference path across core models, reducing reliance on non-ONNX fallbacks and demonstrating depth in deep learning and reinforcement learning integration.