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README.md

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 ---
-license: mit
+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}, 
+}
+```

config.json

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+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "COCONUTGPT2ForTokenClassification"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "latent_end_id": 50258,
+  "latent_id": 50259,
+  "latent_start_id": 50257,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": 50256,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "target_id": 4211,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "vocab_size": 50260
+}

model.safetensors

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