动态拆解让大模型推理更高效，自动分步提升准确率。
DISC: Dynamic Decomposition Improves LLM Inference Scaling
- 推理时自动划分任务步骤，而非固定分法。
- 在APPS等基准上错误率降低5%~10.5%。
- 适合需要高效推理的代码与数学问题求解场景。
大模型推理常通过将问题分解为若干步骤（或词元组）来实现，随后采样并选择最优下一步。然而，这些步骤及其大小通常预先设定或依赖领域知识手动设计。本文提出动态分解方法，在推理过程中自适应、自动地将求解与推理轨迹划分为可管理的步骤。通过更有效地分配计算资源——特别是对困难步骤进行细分并优先采样——动态分解显著提升了推理效率。在APPS、MATH和LiveCodeBench等基准上的实验表明，该方法优于静态分解方式，包括词元级、句子级和单步分解，分别将pass@10错误率降低了5.0%、6.7%和10.5%。这些结果表明，动态分解有望改进多种推理扩展技术。
原文摘要 · Abstract (English)
Inference scaling methods for LLMs often rely on decomposing problems into steps (or groups of tokens), followed by sampling and selecting the best next steps. However, these steps and their sizes are often predetermined or manually designed based on domain knowledge. We propose dynamic decomposition, a method that adaptively and automatically partitions solution and reasoning traces into manageable steps during inference. By more effectively allocating compute -- particularly through subdividing challenging steps and prioritizing their sampling -- dynamic decomposition significantly improves inference efficiency. Experiments on benchmarks such as APPS, MATH, and LiveCodeBench demonstrate that dynamic decomposition outperforms static approaches, including token-level, sentence-level, and single-step decompositions, reducing the pass@10 error rate by 5.0%, 6.7%, and 10.5% respectively. These findings highlight the potential of dynamic decomposition to improve a wide range of inference scaling techniques.
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