title: Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous
    Concept Space
summary: Human cognition typically involves thinking through abstract, fluid concepts
  rather than strictly using discrete linguistic tokens. Current reasoning
  models, however, are constrained to reasoning within the boundaries of human
  language, processing discrete token embeddings that represent fixed points in
  the semantic space. This discrete constraint restricts the expressive power and
  upper potential of such reasoning models, often causing incomplete exploration
  of reasoning paths, as standard Chain-of-Thought (CoT) methods rely on sampling
  one token per step. In this work, we introduce Soft Thinking, a training-free
  method that emulates human-like "soft" reasoning by generating soft, abstract
  concept tokens in a continuous concept space. These concept tokens are created
  by the probability-weighted mixture of token embeddings, which form the
  continuous concept space, enabling smooth transitions and richer
  representations that transcend traditional discrete boundaries. In essence,
  each generated concept token encapsulates multiple meanings from related
  discrete tokens, implicitly exploring various reasoning paths to converge
  effectively toward the correct answer. Empirical evaluations on diverse
  mathematical and coding benchmarks consistently demonstrate the effectiveness
  and efficiency of Soft Thinking, improving pass@1 accuracy by up to 2.48 points
  while simultaneously reducing token usage by up to 22.4% compared to standard
  CoT. Qualitative analysis further reveals that Soft Thinking outputs remain
  highly interpretable and readable, highlighting the potential of Soft Thinking
  to break the inherent bottleneck of discrete language-based reasoning. Code is
  available at https://github.com/eric-ai-lab/Soft-Thinking.
paper_authors._id: 682e8b59a5b1e59c6978645a
paper_authors.name: Zhen Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:22:12.679Z
paper_authors._id: 610c29dc83ad77d8bc9b0211
paper_authors.avatarUrl: /avatars/4e391dacfb00b601f94cfe49af943943.svg
paper_authors.fullname: zhenzhang
paper_authors.type: user
paper_authors.user: namezz
paper_authors._id: 682e8b59a5b1e59c6978645b
paper_authors.name: Xuehai He
paper_authors._id: 682e8b59a5b1e59c6978645c
paper_authors.name: Weixiang Yan
paper_authors._id: 682e8b59a5b1e59c6978645d
paper_authors.name: Ao Shen
paper_authors._id: 682e8b59a5b1e59c6978645e
paper_authors.name: Chenyang Zhao
paper_authors._id: 682e8b59a5b1e59c6978645f
paper_authors.name: Shuohang Wang
paper_authors._id: 682e8b59a5b1e59c69786460
paper_authors.name: Yelong Shen
paper_authors._id: 682e8b59a5b1e59c69786461
paper_authors.name: Xin Eric Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:22:10.030Z
paper_authors._id: 64679a226192d39142245e5e
paper_authors.avatarUrl: /avatars/05abee0b6317f100923936ca2099e9eb.svg
paper_authors.fullname: Xin Eric Wang
paper_authors.type: user
paper_authors.user: xw-eric
paper_title: Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous
    Concept Space
paper_summary: Human cognition typically involves thinking through abstract, fluid concepts
  rather than strictly using discrete linguistic tokens. Current reasoning
  models, however, are constrained to reasoning within the boundaries of human
  language, processing discrete token embeddings that represent fixed points in
  the semantic space. This discrete constraint restricts the expressive power and
  upper potential of such reasoning models, often causing incomplete exploration
  of reasoning paths, as standard Chain-of-Thought (CoT) methods rely on sampling
  one token per step. In this work, we introduce Soft Thinking, a training-free
  method that emulates human-like "soft" reasoning by generating soft, abstract
  concept tokens in a continuous concept space. These concept tokens are created
  by the probability-weighted mixture of token embeddings, which form the
  continuous concept space, enabling smooth transitions and richer
  representations that transcend traditional discrete boundaries. In essence,
  each generated concept token encapsulates multiple meanings from related
  discrete tokens, implicitly exploring various reasoning paths to converge
  effectively toward the correct answer. Empirical evaluations on diverse
  mathematical and coding benchmarks consistently demonstrate the effectiveness
  and efficiency of Soft Thinking, improving pass@1 accuracy by up to 2.48 points
  while simultaneously reducing token usage by up to 22.4% compared to standard
  CoT. Qualitative analysis further reveals that Soft Thinking outputs remain
  highly interpretable and readable, highlighting the potential of Soft Thinking
  to break the inherent bottleneck of discrete language-based reasoning. Code is
  available at https://github.com/eric-ai-lab/Soft-Thinking.
paper_ai_summary: Soft Thinking, a training-free method, enhances reasoning by generating soft, abstract concept tokens in a continuous space, improving accuracy and efficiency in mathematical and coding benchmarks.
paper_ai_keywords: Soft Thinking continuous concept space token embeddings Chain-of-Thought pass@1 accuracy