applied research at prime intellect is both a client-facing team and a fundamental research team. continual learning is our north star. we view this dual approach as necessary in order to study the core challenges faced by models which self-improve in production. the real world is big and messy. we spend part of our our time working with customers to train SOTA models for specialized domains, and we automate and productize our work as we go. we also do open-ended research on novel methods for self-improvement and long-horizon memory, and we get to experiment with data that no other lab gets to see. today, the most readily-deployable form of continual learning is converting "exhaust" (agent traces, logs, feedback) into robust environments and tasks for continual RL. but this is not the final form. there are major open challenges around knowledge acquisition, harness-weight tradeoffs, user modeling, dynamic underspecified preferences, updating and planning across timescales, selective forgetting, data efficiency, learning from golden answers, and company-scale simulation. this is the roadmap, roughly speaking. some of these may require changes to architecture, or parameter-efficient methods, or fundamentally new algorithms. we have our hypotheses and early evidence, but everything is on the table. the world is our oyster. we are betting on a radically different future of what ai deployment will look like, and we can see the path more clearly every day. it is a world where agents learn like humans do, with billions of individual models compounding their learnings from the real world continuously. we're hiring incredibly ambitious researchers in SF, NY, and London. come join us.