title: Accelerate Parallelizable Reasoning via Parallel Decoding within One
    Sequence
summary: Recent advances in reasoning models have demonstrated significant
  improvements in accuracy, particularly for complex tasks such as mathematical
  reasoning, by employing detailed and comprehensive reasoning processes.
  However, generating these lengthy reasoning sequences is computationally
  expensive and time-consuming. To address this inefficiency, we leverage the
  inherent parallelizability of certain tasks to accelerate the reasoning
  process. Specifically, when multiple parallel reasoning branches exist, we
  decode multiple tokens per step using a specialized attention mask, processing
  them within a single sequence, avoiding additional memory usage. Experimental
  results show that our method achieves over 100% speedup in decoding time while
  maintaining the answer quality.
paper_authors._id: 67f62f3a28b4852d4761e842
paper_authors.name: Yijiong Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-09T16:47:00.761Z
paper_authors._id: 6374c494958cd71fa7ea0a9d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6374c494958cd71fa7ea0a9d/2YCKv6tXCZXtsIOFIIXjs.png
paper_authors.fullname: yuyijiong
paper_authors.type: user
paper_authors.user: yuyijiong
paper_title: Accelerate Parallelizable Reasoning via Parallel Decoding within One
    Sequence
paper_summary: Recent advances in reasoning models have demonstrated significant
  improvements in accuracy, particularly for complex tasks such as mathematical
  reasoning, by employing detailed and comprehensive reasoning processes.
  However, generating these lengthy reasoning sequences is computationally
  expensive and time-consuming. To address this inefficiency, we leverage the
  inherent parallelizability of certain tasks to accelerate the reasoning
  process. Specifically, when multiple parallel reasoning branches exist, we
  decode multiple tokens per step using a specialized attention mask, processing
  them within a single sequence, avoiding additional memory usage. Experimental
  results show that our method achieves over 100% speedup in decoding time while
  maintaining the answer quality.
paper_ai_summary: A method using parallelized token decoding with specialized attention masks speeds up reasoning processes by over 100% without degrading answer quality.
paper_ai_keywords: reasoning models mathematical reasoning parallelizability parallel reasoning branches specialized attention mask