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A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems

Paper · 2025 · Zixuan Ke, Fangkai Jiao, Yifei Ming et al.

Summary

Research on LLM reasoning has grown rapidly since chain-of-thought prompting appeared in 2022. Earlier surveys mostly covered test-time techniques for a single model. This survey, by authors from Salesforce AI Research and universities in Singapore and Hong Kong, organizes the field along two independent axes.

  • Regime. This axis separates inference scaling (spending extra compute at test time through prompting, search, planning and candidate verification) from learning to reason (changing model weights through supervised fine-tuning, preference optimization or reinforcement learning).
  • Architecture. This axis contrasts a standalone LLM with agentic setups. These include a single agent using tools such as retrievers, verifiers or code interpreters, and groups of agents that communicate.

Each combination is examined from two sides: the input side (building better prompts and context) and the output side (refining sampled candidates). The authors trace two shifts. One is toward training-based reasoning, exemplified by DeepSeek-R1. The other is toward agentic workflows such as OpenAI Deep Research and Manus. They also outline open problems in evaluation, theory and data. The result is a structured map for choosing and combining reasoning techniques.

Key points

  • Figure 1 tracks cumulative paper counts from 2022 to 2/2025 using Semantic Scholar keyword search. It shows the field accelerating after CoT, ChatGPT, OpenAI o1 (09/2024) and DeepSeek-R1 (01/2025).
  • Candidate evaluation is split into two families: outcome reward models (ORMs), which score final answers, and process reward models (PRMs), which score intermediate steps.
  • The learning methods reviewed include long-CoT SFT, DPO, PPO and GRPO, with separate treatment of training reasoners and training verifiers.
  • Agentic examples include Grok 3 Deep Search and OpenAI Deep Research (both 02/2025) for single agents, and Manus for multi-agent systems. Design patterns covered include generator-evaluator and LLM debate.
  • The open challenges identified are:
    • exhaustion of expert-annotated, verifiable training problems for RL;
    • the cost and ambiguity of process supervision in long CoT;
    • limited theory, such as circuit-complexity results on what transformers can compute with CoT.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

 Fields, topics and concepts

Cite

Cite as Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, Peifeng Wang, Silvio Savarese, Caiming Xiong and Shafiq Joty. 2025. A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems. arXiv:2504.09037.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2504.09037 · arXiv 2504.09037

@misc{ke2025survey,
  title = {{A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems}},
  author = {Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, Peifeng Wang, Silvio Savarese, Caiming Xiong and Shafiq Joty},
  year = {2025},
  doi = {10.48550/arxiv.2504.09037},
  eprint = {2504.09037},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2504.09037}
}

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