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 ![GitHub license](https://img.shields.io/badge/License-MIT-blue.svg)
 [![arXiv](https://img.shields.io/badge/arXiv-1234.56789-b31b1b.svg)](https://arxiv.org/abs/2406.16218)
 
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 Trace is a new AutoDiff-like tool for training AI systems end-to-end with general feedback (like numerical rewards or losses, natural language text, compiler errors, etc.). Trace generalizes the back-propagation algorithm by capturing and propagating an AI system's execution trace. Trace is implemented as a PyTorch-like Python library. Users write Python code directly and can use Trace primitives to optimize certain parts, just like training neural networks!
 
 [Paper](https://arxiv.org/abs/2406.16218) | [Project website](https://microsoft.github.io/Trace/) | [Documentation](https://microsoft.github.io/Trace/intro.html) | [Blogpost](https://www.microsoft.com/en-us/research/blog/tracing-the-path-to-self-adapting-ai-agents/)
 
-![BlogPost](https://github.com/microsoft/Trace/blob/main/docs/images/MSR_blog_cover2.png?raw=true)
 
 ## Setup