A Survey on Progress in LLM Alignment from the Perspective of Reward Design

• URL: http://arxiv.org/abs/2505.02666v2
• Date: Fri, 29 Aug 2025 15:47:44 GMT
• Title: A Survey on Progress in LLM Alignment from the Perspective of Reward Design
• Authors: Miaomiao Ji, Yanqiu Wu, Zhibin Wu, Shoujin Wang, Jian Yang, Mark Dras, Usman Naseem,
• Abstract summary: Reward design plays a pivotal role in aligning large language models with human values, serving as the bridge between feedback signals and model optimization.<n>This survey provides a structured organization of reward modeling and addresses three key aspects: mathematical formulation, construction practices, and interaction with optimization paradigms.
• Score: 29.9792653187187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reward design plays a pivotal role in aligning large language models (LLMs) with human values, serving as the bridge between feedback signals and model optimization. This survey provides a structured organization of reward modeling and addresses three key aspects: mathematical formulation, construction practices, and interaction with optimization paradigms. Building on this, it develops a macro-level taxonomy that characterizes reward mechanisms along complementary dimensions, thereby offering both conceptual clarity and practical guidance for alignment research. The progression of LLM alignment can be understood as a continuous refinement of reward design strategies, with recent developments highlighting paradigm shifts from reinforcement learning (RL)-based to RL-free optimization and from single-task to multi-objective and complex settings.

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