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October 18, 2025Open Access

A Survey on Parallel Reasoning

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Authors

ZWZiqi WangFriedrich-Alexander-Universität Erlangen-NürnbergBNBen NiuChina Pharmaceutical UniversityZGZhiyan GaoGuangzhou Medical University

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Implication

This survey explores parallel reasoning improvements in LLMs, highlighting challenges and potential advancements.

Key Points

  • Parallel reasoning enhances reasoning robustness by exploring multiple thought processes concurrently.
  • The survey defines parallel reasoning and distinguishes it from concepts like chain-of-thought for clarity.
  • Advanced techniques include interactive reasoning, non-interactive reasoning, and efficiency-focused strategies.
  • Core challenges and future research directions for parallel reasoning are highlighted to encourage further studies.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68f408995de60f8893c6ffb4https://doi.org/10.48550/arxiv.2510.12164
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Learning Adaptive Parallel Reasoning with Language Models2025
  2. 2Parallel-R1: Towards Parallel Thinking via Reinforcement Learning2025
  3. 3Toward Efficient and Faithful Reasoning in Large Language Models2025 · 1 citations
  4. 4Reasoning in Large Language Models: A Survey2025
  5. 5ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute2025