Design, run, and analyse large-scale RL post-training experiments (e.g., RLHF, RL from verifiable rewards).
Research and develop methods that improve out-of-distribution generalization, such as reward design, environment and task diversity, curricula, and regularization.
Build held-out evaluations that measure generalization to unseen tasks, domains, and formats, and that catch reward hacking and benchmark overfitting.
Diagnose training dynamics and failure modes, including instability, reward over-optimization, and capability regressions.
Turn successful research into robust, reproducible training recipes for production models.
