MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization

2026-05-20 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

MO-CAPO is a new multi-objective prompt optimization algorithm designed to jointly optimize large language model (LLM) performance and inference cost. Existing methods primarily focus on performance, often overlooking critical factors like cost and latency, while current multi-objective approaches inefficiently use standard algorithms like NSGA-II. MO-CAPO addresses these limitations by incorporating budget allocation for cost-efficient optimization and introducing a deployment-oriented cost objective that captures the full computational profile of LLM inference. Evaluated across four tasks and three LLMs, MO-CAPO consistently identifies robust and diverse Pareto front approximations, outperforming an NSGA-II baseline in 8 out of 12 cases using the noisy R2 metric. It achieves competitive performance, often with a significantly lower budget, and discovers solution sets that offer various performance-cost trade-offs, which single-objective optimizers miss. The study also includes the first evaluation of multi-objective machine learning experiments considering generalization and robustness via noisy R2 and approximation gap.

Key takeaway

For AI Engineers deploying LLMs, MO-CAPO provides a critical tool for balancing model performance with inference costs. You can use this algorithm to efficiently discover a range of prompts that offer different trade-offs, enabling you to select the most suitable prompt for your specific operational budget and performance requirements. This approach helps avoid overspending on inference while maintaining competitive task performance.

Key insights

MO-CAPO optimizes LLM prompts for both performance and inference cost using budget-aware multi-objective techniques.

Principles

• Jointly optimize performance and cost.
• Budget allocation improves optimization efficiency.
• Consider generalization and robustness in evaluation.

Method

MO-CAPO is a multi-objective prompt optimization algorithm that leverages budget allocation and a deployment-oriented cost objective to jointly optimize LLM performance and inference cost.

In practice

• Identify performance-cost trade-offs for LLM deployment.
• Use MO-CAPO to find diverse prompt sets.
• Evaluate solutions with noisy R2 and approximation gap.

Topics

• MO-CAPO
• Prompt Optimization
• Large Language Models
• Multi-Objective Optimization
• Inference Cost

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Prompt Engineer

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• A Human Prompt Is The Most Complex Thing For AI — Current prompt optimization methods fail to identify structural prompt defects, trapping LLMs in local optima.
• Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap — Atropos improves LLM agent cost-benefit by predicting inference failure and hotswapping to a more capable model.
• The Model You Love Is Probably Just the One You Use — Developer preference for LLMs is often driven by familiarity and integration ease, not objective superiority.
• FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines — FAPO autonomously optimizes multi-step LLM pipelines by iteratively diagnosing and refining prompts or structural components.
• Prompt Segmentation and Annotation Optimisation: Controlling LLM Behaviour via Optimised Segment-Level Annotations — PSAO optimizes LLM prompts by segmenting them and adding human-readable annotations to guide model focus.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.