arXiv Machine Learning
Jun 4
CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
arXiv:2606. 04661v1 Announce Type: cross Abstract: Prompts tuned for accuracy often grow long, raising inference cost on every model call.
Explore More, Validate Less: Efficient Reflective Prompt Optimization
The paper introduces a two‑stage framework for reflective prompt optimization that separates candidate generation from validation. In the construction stage, multiple parents are sampled and their training responses reused across parallel reflection branches, with each revision screened on a small minibatch to allow locally improved candidates to propagate without full validation. In the exploration stage, a fixed full‑validation budget is used to select the best prompt from a frozen candidate pool, enabling independent control of pool size and validation effort. Experiments on five benchmarks with Qwen3.5‑9B and Llama‑3.1‑8B‑Instruct show that retaining 100 candidates and performing 20 full validations can improve performance by up to 11.22 percentage points over the initial solver, and learned selection consistently outperforms the initial solver across all comparisons.
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