Samet Oymak

We propose BREAD, a novel and effective variant of GRPO that bridges supervised learning and reinforcement learning by employing branch rollouts from expert traces.

We show that transformers achieve length generalization when training on shorter main task and longer auxiliary tasks together.

We characterize the structure of embeddings obtained via gradient descent, showing that the attention mechanism provably selects important tokens.