Update README.md

README.md

@@ -1,38 +1,38 @@
----
-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}, 
-}
+---
+library_name: transformers
+tags:
+- rm
+- latent
+datasets:
+- openai/gsm8k
+base_model:
+- openai-community/gpt2
+pipeline_tag: token-classification
+---
+
+# latent-tts-rm
+
+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/ModalityDance/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}, 
+}
 ```