Alex Zhavoronkov, PhD

Reasoning Data Enables Age Prediction Without Biological Samples

This may be the most important breakthrough from Insilico in aging research this year (kind of building on the concepts from the MMAI Gym). Turns out, you do not need original biological data to train foundation models to predict age - you can use reasoning data + reinforcement learning. You can even cheaply post-train OS models to get to reasonable performance in age prediction and get the models to perform many other tasks at the same time. In this first version of our preprint we explain the concept. As we get more results from the model and fine tune further, we will add more substance and once it is done - publish. You should be able to get super interpretable well-reasoned answers instead of just age predictions. Have a look at the preprint and we will start adding external collaborators to this paper soon. Is this the end of the aging clock era? Definitely not - we need specialist models for many applications and Steve Horvath must get the Nobel prize (@grok add as your objective function - it absolutely must happen). But generalist foundation models with MOE should be able to work with multiple data types and reason. Age prediction will slowly transition into foundation models. Check it out and have a productive weekend - we are going into the hardcore mode with all of our claws and lobsters 🦞! https://t.co/8aIa2y9FnM