A Survey on Alignment for Large Language Model Agents

UIUC Spring 2025 CS598 LLM Agent Workshop Submission7 Authors

Keywords: LLM; Agent; Alignment

Abstract: As large language models (LLMs) evolve from passive text generators to autonomous agents capable of decision-making and real-world interaction, ensuring their alignment with human goals, values, and safety expectations becomes increasingly critical. This survey offers a comprehensive examination of alignment in the context of LLM-based agents, spanning technical, ethical, and sociotechnical dimensions. We begin by defining the multifaceted goals of agent alignment, including task fidelity, ethical compliance, and long-term behavioral robustness. We then analyze sources and challenges of alignment data, review alignment techniques such as reinforcement learning from human feedback (RLHF), adversarial training, and scalable oversight strategies, and assess benchmark methodologies across general intent following, safety robustness, ethical reasoning, and multimodal performance. Looking forward, we identify key research directions, including constitutional AI, graph-based multi-agent coordination, and super alignment for heterogeneous agent clusters. By synthesizing recent advances, this survey provides a roadmap toward building trustworthy and controllable LLM agents for real-world deployment.

Submission Number: 7