Vmax is automating reinforcement learning. We transform proprietary data and evals into new sets of environments. We refine agents on new examples of the tasks they are intended to perform. Backed by South Park Commons.
About the role
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.
What they're looking for
- Track record of research excellence or strong research promise, demonstrated through publications, preprints, open-source work, technical projects, competitions, or publicly available artifacts
- Working understanding of reinforcement learning
- Familiarity with unsupervised/automated environment design, asymmetric self-play, and/or intrinsic motivation
- Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX
- Clear written and verbal communication of technical ideas
More about this role
V max is an applied research lab developing AI capable of open-ended learning. We are building systems to exceed humans in all capacities by optimising beyond the local maxima of learning from human expertise.
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.
- Investigate how...
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