Introducing Adjoint Sampling, a new learning algorithm that trains generative models based on scalar rewards. Based on theoretical foundations developed by FAIR, Adjoint Sampling leads to a highly scalable practical algorithm, and can become the foundation for further research into highly scalable sampling methods. Read our research paper on... Adjoint Sampling and download the model, code, and benchmark ➡️show more

how long before you can apply AI methods to human schooling, to replace current snails pace obedience training?

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Scalable sampling, promising research 👀

this is fascinating, can't wait to dig into the paper

Exciting potential! How does it handle real-time data optimization?

Meta is a slave like company.

Adjoint Sampling is a new way to train models using simple rewards. It’s practical and based on solid research.

Finally, a way to train models that won't ghost me after the first date!