Gradient-Guided Multi-Judge Prompt Optimization

Automatic prompt optimization is a practical alternative to fine-tuning for adapting large language models (LLMs), yet existing approaches often trade off signal quality against computational cost. Methods that rely on generative feedback can be informative but expensive to scale, while sampling-based optimization typically requires many evaluations and exhibits high variance. Even loss-driven prompt optimization remains limited by costly segment attribution that scales with prompt length and by overfitting to a single evaluator, which weakens transfer across model families and domains. We propose Gradient-guided Multi-judge Prompt Optimization (GMPO), a scalable framework that improves both efficiency and robustness. GMPO uses a firstorder gradient approximation to score segment importance in a continuous masking direction, requiring only one forward and one backward pass. GMPO further employs a generate multi-judge design in which candidate prompt edits are proposed by a generator and selected using cross-entropy losses aggregated from multiple lightweight judge models, reducing evaluator bias and improving generalization. Experiments across math, reasoning, instruction-following evaluation, and safety robustness benchmarks demonstrate consistent gains with substantially lower optimization overhead. Our code is available at https://github.com/cyzcz/GMPO . * Corresponding author. * "X days have passed since [date]" today = [date] plus X days → -Always explicitly state what "today" is before proceeding → 3. PARSE TEMPORAL RELATIONSHIPS -Understand directional phrases: * "before" means subtract days * "after" means add days * "since" typically means counting forward from a past date → -Be precise about whether events are described relative to "today" or to other dates → 4. PERFORM DATE CALCULATIONS -Use correct month lengths: