Arip Asadulaev

You begin by making work that is simple and bad; then complex and bad; then complex and good; and finally, simple and good This quote is often attributed to the painter Ilya Repin. I’ve found this pattern shows up far beyond painting, and I judge ideas through this prism.
Quality versus Complexity: a learning loop Axes: Complexity increases to the right, Quality increases upward. Corners: bad & simple, bad & complex, good & complex, good & simple. Path: bad-simple to bad-complex to good-complex to good-simple. Complexity Quality simple & bad complex & bad complex & good simple & good

Working on reinforcement learning and generative models.

Simple & good

Zero-shot off-policy learning.

Complex & good

Your latent reasoning is secretly policy improvement operator.

Y-shaped generative flows.

Complex & bad

Rethinking optimal transport in offline reinforcement learning.

Neural optimal transport with general cost functionals.

Simple & bad

A minimalist approach for domain adaptation with optimal transport.

Exploring and exploiting conditioning of reinforcement learning agents.

See all my papers here.

Other contributions:

Applications: Designed neural computer architectures that generate molecules with favorable pharmacokinetics. These models were later validated in real-world applications.

Developed a CowSwap solver and built various AI layers to optimize token swaps, asset bridging, and other decentralized finance operations.

Teaching: Created and taught several courses on reinforcement learning and deep generative models, covering a range of topics from GANs to flow-matching.

Open to discussing new ideas and potential collaborations. Feel free to contact.

Social: @machinestein

Last update: Feb 20, 2026