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October 1, 2025Open Access

Learning Adaptive Parallel Reasoning with Language Models

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Authors

JPJiayi PanXLXiuyu LiLLLong Lian

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Overview

Proposed framework improves reasoning outcomes in language models, demonstrating higher performance and better scalability.

Key Points

  • Adaptive Parallel Reasoning achieves 83.4% performance compared to 60.0% within the same context window.
  • Increased computation led to 80.1% accuracy versus 66.6% at 20k total tokens.
  • End-to-end reinforcement learning optimizes reasoning processes and improves task success rates.
  • Framework enables efficient coordination of serialized and parallel computations in one process.

Cite This Study

Pan et al. (2025) studied this question.

synapsesocial.com/papers/68dd91c7fe798ba2fc498489https://doi.org/10.48550/arxiv.2504.15466
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Also Consider

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

  1. 1A Survey on Parallel Reasoning2025
  2. 2Interleaved Reasoning for Large Language Models via Reinforcement Learning2025
  3. 3ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute2025
  4. 4Parallel-R1: Towards Parallel Thinking via Reinforcement Learning2025
  5. 5Parallel Scaling Law: Unveiling Reasoning Generalization through A Cross-Linguistic Perspective2025