複合式人工智慧系統優化：方法、挑戰與未來方向綜述

摘要

近期大型語言模型（LLMs）與人工智慧系統的進展，已引領複雜AI工作流程設計與優化的範式轉變。透過整合多個組件，複合式AI系統在執行精細任務上日益嫻熟。然而，隨著這些系統複雜度的提升，不僅在單一組件的優化上，其間互動的優化亦面臨新的挑戰。儘管監督式微調（SFT）與強化學習（RL）等傳統優化方法仍為基礎，自然語言回饋的興起為優化不可微分系統引入了前景光明的新途徑。本文系統性地回顧了複合式AI系統優化的最新進展，涵蓋數值與基於語言的技術。我們正式定義了複合式AI系統優化的概念，沿著幾個關鍵維度對現有方法進行分類，並強調這一快速發展領域中的開放研究挑戰與未來方向。所調查文獻的清單公開於https://github.com/MiuLab/AISysOpt-Survey。

English

Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept at performing sophisticated tasks. However, as these systems grow in complexity, new challenges arise in optimizing not only individual components but also their interactions. While traditional optimization methods such as supervised fine-tuning (SFT) and reinforcement learning (RL) remain foundational, the rise of natural language feedback introduces promising new approaches, especially for optimizing non-differentiable systems. This paper provides a systematic review of recent progress in optimizing compound AI systems, encompassing both numerical and language-based techniques. We formalize the notion of compound AI system optimization, classify existing methods along several key dimensions, and highlight open research challenges and future directions in this rapidly evolving field. A list of surveyed papers is publicly available at https://github.com/MiuLab/AISysOpt-Survey.

複合式人工智慧系統優化：方法、挑戰與未來方向綜述

Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions

摘要

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