Iterative prompt refinement is a practical approach for improving the reliability of large language models without weight updates. In this work, we study metatuning : a judge-guided prompt-refinement loop in which an evaluator critiques errors and provides targeted natural-language corrections or demonstrations that are incorporated into the prompt. We evaluate metatuning on axiomatic deductive reasoning (MATH-500), on combinations with chain-of-thought and self-reflection prompting, and on video-based physical reasoning (CLEVRER). Our results show that metatuning can improve baseline performance in static, rule-like domains, but offers limited benefit when paired with strong reasoning baselines and does not generalize to spatiotemporal video reasoning. Overall, we identify boundary conditions for judge-guided prompt refinement and motivate future work on integrating feedback at the level of reasoning traces.
Chattopadhyay et al. (2026) studied this question.