Research Fellowship - Open Endedness

Vmax is an applied research lab developing AI systems capable of open-ended learning. The fellowship focuses on designing and building reinforcement learning methods for LLM agents that discover their own objectives, tasks, and curricula, while owning a focused research project and contributing to publications.

Responsibilities:

• 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
• Investigate how agents can avoid collapse into trivial, degenerate, or easily exploitable objectives
• Own and develop a research agenda within Vmax, from identifying promising directions to executing experiments and communicating results