Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous
  Concept Space
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.
682e8b59a5b1e59c6978645a
Zhen Zhang
claimed_verified
2025-05-26T08:22:12.679Z
610c29dc83ad77d8bc9b0211
/avatars/4e391dacfb00b601f94cfe49af943943.svg
zhenzhang
user
namezz
682e8b59a5b1e59c6978645b
Xuehai He
682e8b59a5b1e59c6978645c
Weixiang Yan
682e8b59a5b1e59c6978645d
Ao Shen
682e8b59a5b1e59c6978645e
Chenyang Zhao
682e8b59a5b1e59c6978645f
Shuohang Wang
682e8b59a5b1e59c69786460
Yelong Shen
682e8b59a5b1e59c69786461
Xin Eric Wang
claimed_verified
2025-05-26T08:22:10.030Z
64679a226192d39142245e5e
/avatars/05abee0b6317f100923936ca2099e9eb.svg
Xin Eric Wang
user
xw-eric
Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous
  Concept Space
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.
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.
Soft Thinking continuous concept space token embeddings Chain-of-Thought pass@1 accuracy