GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler
arXiv:2602.14077v1 Announce Type: new Abstract: Inference-time scaling (ITS) in latent reasoning models typically introduces stochasticity through heuristic perturbations, such as dropout or fixed Gaussian noise. While these methods increase trajectory diversity, their exploration behavior is not explicitly modeled and can be inefficient under finite sampling budgets. We observe that stronger perturbations do not necessarily translate into more effective candidate trajectories, as unguided noise may disrupt internal decision structure rather than steer it. To provide a more structured alternative, we model latent thought exploration as conditional sampling from learnable densities and instantiate this idea as a Gaussian Thought Sampler (GTS). GTS predicts context-dependent perturbation distributions over continuous reasoning states and is trained with GRPO-style policy optimization while keeping the backbone frozen. Experiments on GSM8K with two latent reasoning architectures show tha
arXiv:2602.14077v1 Announce Type: new Abstract: Inference-time scaling (ITS) in latent reasoning models typically introduces stochasticity through heuristic perturbations, such as dropout or fixed Gaussian noise. While these methods increase trajectory diversity, their exploration behavior is not explicitly modeled and can be inefficient under finite sampling budgets. We observe that stronger perturbations do not necessarily translate into more effective candidate trajectories, as unguided noise may disrupt internal decision structure rather than steer it. To provide a more structured alternative, we model latent thought exploration as conditional sampling from learnable densities and instantiate this idea as a Gaussian Thought Sampler (GTS). GTS predicts context-dependent perturbation distributions over continuous reasoning states and is trained with GRPO-style policy optimization while keeping the backbone frozen. Experiments on GSM8K with two latent reasoning architectures show that GTS achieves more reliable inference-time scaling than heuristic baselines. These findings indicate that improving latent ITS requires structured and optimizable exploration mechanisms rather than simply amplifying stochasticity.
Executive Summary
The article 'GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler' introduces a novel approach to inference-time scaling (ITS) in latent reasoning models. Traditional methods, such as dropout or fixed Gaussian noise, introduce stochasticity to increase trajectory diversity but lack explicit modeling of exploration behavior, leading to inefficiencies. The authors propose a Gaussian Thought Sampler (GTS) that models latent thought exploration as conditional sampling from learnable densities. GTS predicts context-dependent perturbation distributions over continuous reasoning states and is trained using GRPO-style policy optimization while keeping the backbone model frozen. Experiments on the GSM8K dataset with two latent reasoning architectures demonstrate that GTS achieves more reliable inference-time scaling compared to heuristic baselines. The findings suggest that structured and optimizable exploration mechanisms are crucial for improving latent ITS, rather than merely amplifying stochasticity.
Key Points
- ▸ Traditional ITS methods introduce stochasticity through heuristic perturbations, which can be inefficient.
- ▸ GTS models latent thought exploration as conditional sampling from learnable densities.
- ▸ GTS predicts context-dependent perturbation distributions and is trained with GRPO-style policy optimization.
- ▸ Experiments show GTS achieves more reliable ITS than heuristic baselines.
- ▸ Structured and optimizable exploration mechanisms are key to improving latent ITS.
Merits
Innovative Approach
The article introduces a novel method for inference-time scaling that moves beyond traditional heuristic perturbations, offering a more structured and optimizable approach.
Empirical Validation
The findings are supported by experiments on the GSM8K dataset, demonstrating the effectiveness of GTS compared to baseline methods.
Practical Applicability
The proposed method can be applied to various latent reasoning architectures, making it versatile and broadly applicable.
Demerits
Limited Scope
The experiments are conducted on a single dataset (GSM8K), which may limit the generalizability of the findings to other domains or datasets.
Complexity
The method involves complex training procedures and may require significant computational resources, which could be a barrier to adoption.
Backbone Model Constraints
The requirement to keep the backbone model frozen during training might limit the flexibility and adaptability of the approach in certain scenarios.
Expert Commentary
The article presents a significant advancement in the field of inference-time scaling for latent reasoning models. By introducing the Gaussian Thought Sampler (GTS), the authors address a critical limitation of traditional methods, which often rely on heuristic perturbations that lack explicit modeling of exploration behavior. The proposed approach not only offers a more structured and optimizable framework but also demonstrates empirical validation through experiments on the GSM8K dataset. The findings underscore the importance of context-dependent perturbation distributions in achieving reliable inference-time scaling. However, the method's complexity and the requirement to keep the backbone model frozen during training may pose challenges for widespread adoption. Future research could explore the applicability of GTS to other datasets and domains, as well as potential modifications to enhance its flexibility and adaptability. Overall, this work provides valuable insights and sets a new benchmark for improving latent reasoning models through structured exploration mechanisms.
Recommendations
- ✓ Further research should investigate the applicability of GTS to a broader range of datasets and domains to validate its generalizability.
- ✓ Exploring modifications to the training procedure to allow for more flexible adaptation of the backbone model could enhance the method's versatility.
