Explore More, Validate Less: Efficient Reflective Prompt Optimization
Reflective prompt optimization improves instructions using task feedback, but coupling candidate generation with full-validation-based selection can make search costly and sequential. We propose a two-stage framework that decouples reflective candidate-pool construction from validation allocation. During construction, we sample multiple parents and reuse each parent's training responses across parallel reflection branches. Each revision is screened against its parent on a small training minibatch, allowing locally improved candidates to support further revisions without full validation. After construction, an exploration policy selects candidates from the frozen pool under a fixed full-va...
| Authors | Xiaofan Zhou, Lu Cheng |
|---|---|
| Primary category | cs.CL |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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