---
library_name: transformers
tags:
- rm
- latent
datasets:
- openai/gsm8k
base_model:
- openai-community/gpt2
pipeline_tag: token-classification
---

# LatentRM

The Latent Reward Model (LatentRM) is a learned scorer designed for latent reasoning models that reason in continuous hidden space.
LatentRM provides the missing aggregation signal for parallel test-time scaling in latent models, enabling techniques such as best-of-N and beam search without explicit token-level probabilities.

<p align="center">
  <a href="https://arxiv.org/pdf/2510.07745"><b>Paper Link</b>👁️</a>
</p>

<p align="center">
  <a href="https://github.com/YRYangang/LatentTTS"><b>GitHub Repo</b>🐙</a>
</p>


## Citation
```
@misc{you2025paralleltesttimescalinglatent,
      title={Parallel Test-Time Scaling for Latent Reasoning Models}, 
      author={Runyang You and Yongqi Li and Meng Liu and Wenjie Wang and Liqiang Nie and Wenjie Li},
      year={2025},
      eprint={2510.07745},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.07745}, 
}
```