Research Fellowship - Open Endedness

A core focus of ours is agents that can learn to find their own objectives in the world. We are looking for researchers to design and build new ways of using RL where the formulation of rewards and tasks need to be discovered, rather than given.

This 3 to 6 month fellowship is for PhD students or equivalent early-career researchers who want to work on LLMs that can learn in open-ended settings. You will own a focused research project, work closely with Vmax technical staff, and contribute to research publications.

• Develop RL methods for agents that can discover useful objectives, tasks and curricula without relying entirely on human-specified rewards.
• Design systems for open-ended learning, including unsupervised/automated environment design, asymmetric self-play, and intrinsic motivation.
• Build training loops where agents learn from interaction, exploration, novelty, competence progress, self-generated challenges, or other nonstandard reward signals.