Recursive Reward Improvement via Adversarial Meta-Prompt Evolution

The paper introduces Recursive Reward Improvement (RRI), a method that enhances recursive self‑improvement loops by using an adversary to expose and fix weaknesses in model‑generated rubrics for open‑ended tasks. In RRI, an adversary crafts a rubric‑gaming answer, an honest answer is generated without rubric exposure, and up to two repair steps adjust the rubric so the honest answer scores higher. Experiments on HealthBench Professional show that RRI boosts adjusted scores by up to 12.4 points on a 27B model and yields significant gains on difficult and writing tasks, while also improving performance on unseen benchmarks PRBench Hard and ProfBench.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• reinforcement-learning
• safety