SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning
Test-Time Scaling (TTS) refers to approaches that improve reasoning
performance by allocating extra computation during inference, without altering
the model's parameters. While existing TTS methods operate in a discrete token
space by generating more intermediate steps, recent studies in Coconut and
SoftCoT have demonstrated that thinking in the continuous latent space can
further enhance the reasoning performance. Such latent thoughts encode
informative thinking without the information loss associated with
autoregressive token generation, sparking increased interest in
continuous-space reasoning. Unlike discrete decoding, where repeated sampling
enables exploring diverse reasoning paths, latent representations in continuous
space are fixed for a given input, which limits diverse exploration, as all
decoded paths originate from the same latent thought. To overcome this
limitation, we introduce SoftCoT++ to extend SoftCoT to the Test-Time Scaling
paradigm by enabling diverse exploration of thinking paths. Specifically, we
perturb latent thoughts via multiple specialized initial tokens and apply
contrastive learning to promote diversity among soft thought representations.
Experiments across five reasoning benchmarks and two distinct LLM architectures
demonstrate that SoftCoT++ significantly boosts SoftCoT and also outperforms
SoftCoT with self-consistency scaling. Moreover, it shows strong compatibility
with conventional scaling techniques such as self-consistency. Source code is
available at https://github.com/xuyige/SoftCoT.
682b7826e9f4a26b02e74091
Yige Xu
claimed_verified
2025-05-20T07:22:07.334Z
6448d7e5e87a77e872e47982
/avatars/7405ceef3bf7468cb3e977c4669d81a4.svg
Yige Xu
user
xuyige
682b7826e9f4a26b02e74092
Xu Guo
claimed_verified
2025-05-21T15:17:49.054Z
5f3b74d7a4dd343b63a6009f
/avatars/198d38cad20ea38f47f53e1433d9607c.svg
Xu Guo
user
xuxuxu
682b7826e9f4a26b02e74093
Zhiwei Zeng
admin_assigned
2025-05-20T08:52:04.331Z
664b5d83edcadf9fa5e0615d
/avatars/5fdfc87a78b68f1eb54e1ed7d144952a.svg
zeng zhiwei
user
Aver3
682b7826e9f4a26b02e74094
Chunyan Miao
SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning
Test-Time Scaling (TTS) refers to approaches that improve reasoning
performance by allocating extra computation during inference, without altering
the model's parameters. While existing TTS methods operate in a discrete token
space by generating more intermediate steps, recent studies in Coconut and
SoftCoT have demonstrated that thinking in the continuous latent space can
further enhance the reasoning performance. Such latent thoughts encode
informative thinking without the information loss associated with
autoregressive token generation, sparking increased interest in
continuous-space reasoning. Unlike discrete decoding, where repeated sampling
enables exploring diverse reasoning paths, latent representations in continuous
space are fixed for a given input, which limits diverse exploration, as all
decoded paths originate from the same latent thought. To overcome this
limitation, we introduce SoftCoT++ to extend SoftCoT to the Test-Time Scaling
paradigm by enabling diverse exploration of thinking paths. Specifically, we
perturb latent thoughts via multiple specialized initial tokens and apply
contrastive learning to promote diversity among soft thought representations.
Experiments across five reasoning benchmarks and two distinct LLM architectures
demonstrate that SoftCoT++ significantly boosts SoftCoT and also outperforms
SoftCoT with self-consistency scaling. Moreover, it shows strong compatibility
with conventional scaling techniques such as self-consistency. Source code is
available at https://github.com/xuyige/SoftCoT.
SoftCoT++ enhances continuous-space reasoning in Test-Time Scaling by introducing diverse exploration through perturbed latent thoughts and contrastive learning.
Test-Time Scaling discrete token space continuous latent space SoftCoT latent thoughts autoregressive token generation continuous-space reasoning self-consistency scaling contrastive learning LLM architectures