Parallel Test-Time Scaling for Latent Reasoning Models
Authors: name: Runyang You, name: Yongqi Li, name: Meng Liu, name: Wenjie Wang, name: Liqiang Nie, name: Wenjie Li
Organization: ModalityDance

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.