对比两种推理方式：显式链式思考与隐式潜空间推理的效率差异。
A Formal Comparison Between Chain of Thought and Latent Thought
- 潜空间推理支持更高效的并行计算，链式思考则依赖顺序生成。
- 链式思考通过随机解码实现近似计数与采样，潜空间推理不具备此能力。
- 研究为选择合适推理范式提供理论依据，适合模型架构设计者参考。
链式思考（CoT）通过显式生成中间标记来激发大语言模型的推理能力，而潜空间推理直接在连续潜空间中进行计算，突破了离散语言表示的限制。尽管两者均利用迭代计算，其相对能力仍缺乏系统探讨。本文提出形式化分析，表明潜空间推理比固有顺序的链式思考具有更高的并行计算效率。相反，链式思考可通过随机解码实现近似计数与采样。这些差异揭示了深度递归更适合的任务类型，为推理范式的选择提供了实践指导。
原文摘要 · Abstract (English)
Chain of thought (CoT) elicits reasoning in large language models by explicitly generating intermediate tokens. In contrast, latent thought reasoning operates directly in the continuous latent space, enabling computation beyond discrete linguistic representations. While both approaches exploit iterative computation, their comparative capabilities remain underexplored. In this work, we present a formal analysis showing that latent thought admits more efficient parallel computation than inherently sequential CoT. In contrast, CoT enables approximate counting and sampling through stochastic decoding. These separations suggest the tasks for which depth-driven recursion is more suitable, thereby offering practical guidance for choosing between reasoning paradigms.
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