What Should the Reflector See? An Empirical Study of Evidence in Reflective Prompt Optimization

Title:What Should the Reflector See? An Empirical Study of Evidence in Reflective Prompt Optimization

Abstract:Reflective prompt optimization revises instructions using examples of a model's behavior, but which evidence the reflector should receive remains unclear. We study evidence composition, visibility of examples, candidate selection and domain-knowledge policy within a single-parent Pareto-guided search. Using Qwen3.5-9B as both task model and reflector, we evaluate nine reflection strategies on five datasets. From these experiments, we find three distinct patterns. For performance improvement, Failures-only produces the largest mean test gain (+8.0 percentage points), while Balanced-mix and No-examples+Val share the best mean performance rank. For reflection effectiveness, No-examples achieves the best rank for improving sampled parents, yet yields only a 1.4-point mean test gain: local reflection success does not necessarily produce a stronger final prompt. For overfitting assessment, Failures-only and Balanced-mix share the lowest mean calibration-gap rank, while larger gaps on GPQA and IFBench show that calibration gains can overstate held-out improvement. This gap is a descriptive indicator, not a direct measure of overfitting. Together, these results show why reflection strategies should be assessed separately on final performance, parent improvement and calibration-to-test transfer.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.32452 [cs.CL]

(or arXiv:2609.32452v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.32452 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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