Parallel Test-Time Scaling for Latent Reasoning Models
Parallel test-time scaling (TTS) is a pivotal approach for enhancing large
language models (LLMs), typically by sampling multiple token-based
chains-of-thought in parallel and aggregating outcomes through voting or
search. Recent advances in latent reasoning, where intermediate reasoning
unfolds in continuous vector spaces, offer a more efficient alternative to
explicit Chain-of-Thought, yet whether such latent models can similarly benefit
from parallel TTS remains open, mainly due to the absence of sampling
mechanisms in continuous space, and the lack of probabilistic signals for
advanced trajectory aggregation. \ This work enables parallel TTS for latent
reasoning models by addressing the above issues. For sampling, we introduce two
uncertainty-inspired stochastic strategies: Monte Carlo Dropout and Additive
Gaussian Noise. For aggregation, we design a Latent Reward Model (LatentRM)
trained with step-wise contrastive objective to score and guide latent
reasoning. Extensive experiments and visualization analyses show that both
sampling strategies scale effectively with compute and exhibit distinct
exploration dynamics, while LatentRM enables effective trajectory selection.
Together, our explorations open a new direction for scalable inference in
continuous spaces. Code released at https://github.com/YRYangang/LatentTTS.
68ec636dcd07fb414898c981
Runyang You
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2025-10-13T10:05:39.701Z
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Runyang
user
dd101bb
68ec636dcd07fb414898c982
Yongqi Li
68ec636dcd07fb414898c983
Meng Liu
68ec636dcd07fb414898c984
Wenjie Wang
68ec636dcd07fb414898c985
Liqiang Nie
68ec636dcd07fb414898c986
Wenjie Li
Parallel Test-Time Scaling for Latent Reasoning Models
Parallel test-time scaling (TTS) is a pivotal approach for enhancing large
language models (LLMs), typically by sampling multiple token-based
chains-of-thought in parallel and aggregating outcomes through voting or
search. Recent advances in latent reasoning, where intermediate reasoning
unfolds in continuous vector spaces, offer a more efficient alternative to
explicit Chain-of-Thought, yet whether such latent models can similarly benefit
from parallel TTS remains open, mainly due to the absence of sampling
mechanisms in continuous space, and the lack of probabilistic signals for
advanced trajectory aggregation. \ This work enables parallel TTS for latent
reasoning models by addressing the above issues. For sampling, we introduce two
uncertainty-inspired stochastic strategies: Monte Carlo Dropout and Additive
Gaussian Noise. For aggregation, we design a Latent Reward Model (LatentRM)
trained with step-wise contrastive objective to score and guide latent
reasoning. Extensive experiments and visualization analyses show that both
sampling strategies scale effectively with compute and exhibit distinct
exploration dynamics, while LatentRM enables effective trajectory selection.
Together, our explorations open a new direction for scalable inference in
continuous spaces. Code released at https://github.com/YRYangang/LatentTTS.
Parallel test-time scaling is enabled for latent reasoning models using uncertainty-inspired sampling strategies and a Latent Reward Model for effective trajectory selection.
Parallel test-time scaling large language models token-based chains-of-thought latent reasoning continuous vector spaces Monte Carlo Dropout Additive Gaussian Noise Latent Reward Model step-wise contrastive objective scalable inference