A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems
Ke, Zixuan, Jiao, Fangkai, Ming, Yifei, Nguyen, Xuan-Phi, Xu, Austin, Long, Do Xuan, Li, Minzhi, Qin, Chengwei, Wang, Peifeng, Savarese, Silvio, Xiong, Caiming, Joty, Shafiq
–arXiv.org Artificial Intelligence
Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making. With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems from conventional models that empower chatbots. In this survey, we categorize existing methods along two orthogonal dimensions: (1) Regimes, which define the stage at which reasoning is achieved (either at inference time or through dedicated training); and (2) Architectures, which determine the components involved in the reasoning process, distinguishing between standalone LLMs and agentic compound systems that incorporate external tools, and multi-agent collaborations. Within each dimension, we analyze two key perspectives: (1) Input level, which focuses on techniques that construct high-quality prompts that the LLM condition on; and (2) Output level, which methods that refine multiple sampled candidates to enhance reasoning quality. This categorization provides a systematic understanding of the evolving landscape of LLM reasoning, highlighting emerging trends such as the shift from inference-scaling to learning-to-reason (e.g., DeepSeek-R1), and the transition to agentic workflows (e.g., OpenAI Deep Research, Manus Agent). Additionally, we cover a broad spectrum of learning algorithms, from supervised fine-tuning to reinforcement learning such as PPO and GRPO, and the training of reasoners and verifiers. We also examine key designs of agentic workflows, from established patterns like generator-evaluator and LLM debate to recent innovations. Finally, we identify emerging trends, such as domain-specific reasoning systems, and open challenges, such as evaluation and data quality. This survey aims to provide AI researchers and practitioners with a comprehensive foundation for advancing reasoning in LLMs, paving the way for more sophisticated and reliable AI systems. 1
arXiv.org Artificial Intelligence
Aug-6-2025
- Country:
- Asia
- China > Jiangsu Province
- Yancheng (0.04)
- Indonesia > Bali (0.04)
- Middle East
- Jordan (0.04)
- UAE > Abu Dhabi Emirate
- Abu Dhabi (0.04)
- Myanmar > Tanintharyi Region
- Dawei (0.04)
- Singapore (0.04)
- Thailand > Bangkok
- Bangkok (0.04)
- China > Jiangsu Province
- Europe
- Ireland > Leinster
- County Dublin > Dublin (0.04)
- France > Île-de-France
- Croatia > Dubrovnik-Neretva County
- Dubrovnik (0.04)
- Ukraine > Kyiv Oblast
- Kyiv (0.04)
- Slovenia > Drava
- Municipality of Benedikt > Benedikt (0.04)
- United Kingdom > England
- Cambridgeshire > Cambridge (0.04)
- Monaco (0.04)
- Middle East > Malta
- Eastern Region > Northern Harbour District > St. Julian's (0.04)
- Germany > Berlin (0.04)
- Austria > Vienna (0.14)
- Ireland > Leinster
- North America
- Canada > Ontario
- Toronto (0.04)
- Mexico > Mexico City
- Mexico City (0.04)
- United States
- Florida > Miami-Dade County
- Miami (0.14)
- Louisiana > Orleans Parish
- New Orleans (0.04)
- New Mexico > Bernalillo County
- Albuquerque (0.04)
- New York > New York County
- New York City (0.04)
- Pennsylvania > Allegheny County
- Pittsburgh (0.04)
- Washington > King County
- Seattle (0.04)
- Florida > Miami-Dade County
- Canada > Ontario
- South America
- Asia
- Genre:
- Overview (1.00)
- Research Report > New Finding (1.00)
- Industry:
- Health & Medicine (1.00)
- Information Technology (0.67)
- Leisure & Entertainment > Games (0.45)
- Technology: