Why does the academic community prefer PyTorch for new AI research papers?

All answers to this question.

Researchers love PyTorch because it doesn't get in the way of their ideas. When you're inventing a brand new type of attention mechanism or a strange loss function, you need to be able to manipulate tensors with high granularity. PyTorch’s imperative nature allows you to write "messy" experimental code that just works. In 2024, the "network effect" is also huge—if a researcher wants their paper to be cited and built upon, they provide PyTorch code because that’s what the rest of the community is already using. It’s a self-reinforcing cycle that has made PyTorch the undisputed language of AI innovation.

   Answered 2025-02-24 by Claudia Briggs

Does this mean TensorFlow is becoming a "legacy" skill? If all the new breakthroughs are happening in PyTorch, will a TensorFlow expert be able to keep up with Generative AI and LLM advancements in the next few years?

   Answered 2025-02-25 by Terrance Vance

• Terrance, "legacy" is a strong word, but "specialized" might fit better. TensorFlow is becoming the framework for maintaining massive existing systems, while PyTorch is for building the future. If you want to work on the cutting edge of LLMs or Diffusion models, you almost have to know PyTorch to even read the latest source code. However, the concepts (backprop, layers, optimizers) are the same in both!

     Commented 2025-02-26 by Elena Rossi

PyTorch is built for flexibility. Research is all about breaking things and trying new shapes. TensorFlow’s rigid graph structure just makes that process more painful than it needs to be.

   Answered 2025-02-27 by Julian Hayes

• Exactly, Julian. The ability to change the model architecture mid-loop is something researchers take for granted in PyTorch that is a nightmare in other frameworks.

     Commented 2025-02-28 by Claudia Briggs