Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence
Yijiong Yu
2025-04-09
Summary
This paper talks about a new way to make AI solve math problems faster by letting it think about multiple steps at the same time instead of one-by-one.
What's the problem?
Current AI models solve complex problems step-by-step, which is slow and uses a lot of computing power, especially for tasks like math where many steps are needed.
What's the solution?
The method allows AI to process several parts of a problem simultaneously by using a smart technique to handle multiple steps in one go without needing extra memory.
Why it matters?
This speeds up AI tools for tasks like homework help or data analysis, making them faster and cheaper to run while keeping answers just as accurate.
Abstract
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.