Alborz Geramifard

TREK transforms the way models tackle challenging prompts by expanding their exploration support, leading to substantial performance gains even in the hardest task scenarios.

Role-typed credit assignment can drastically improve reinforcement learning outcomes by accurately distinguishing between useful exploration and regression in agent actions.

Overconfident tokens, often missed by entropy-based methods, carry surprisingly dense corrective signals in on-policy distillation, allowing for near-baseline performance with <10% of tokens.