Research Scientist, Scaling RL

Periodic Labs is an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. The Research Scientist will study how reinforcement learning scales with training compute, develop improved algorithms, and apply research ideas to large-scale frontier model training runs.

Responsibilities:

• Design experiments to understand how RL performance scales with compute, model size, data, and reward quality, building on work such as ScaleRL
• Develop better RL algorithms, spanning policy optimization, advantage estimation, exploration, and credit assignment for long-horizon RL tasks
• Build adaptive sampling and curriculum methods that adjust task difficulty, problem selection, and the number of rollouts as models improve
• Study bias and stability during RL training, including importance-sampling corrections and methods to tackle policy staleness and training–inference mismatch, as discussed here
• Improve compute efficiency across training and inference through experiments with hyperparameters, such as length penalties, rollout counts, batch sizes, and update schedules