Accelerate Parallelizable Reasoning via Parallel Decoding within One
  Sequence
Authors: name: Yijiong Yu

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.