| | --- |
| | base_model: |
| | - princeton-nlp/Sheared-LLaMA-1.3B |
| | datasets: |
| | - ChilleD/MultiArith |
| | license: llama2 |
| | pipeline_tag: text-generation |
| | library_name: pytorch |
| | arxiv: 2510.24940 |
| | tags: |
| | - model_hub_mixin |
| | - pytorch_model_hub_mixin |
| | - chain-of-thought |
| | - implicit-reasoning |
| | --- |
| | |
| | # SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens |
| |
|
| | ## 🚀 Overview |
| | **SemCoT** is a framework that improves the efficiency of Chain-of-Thought (CoT) reasoning by encoding reasoning steps inside hidden representations ("implicit tokens") instead of generating long textual explanations. This approach significantly speeds up inference while maintaining high reasoning performance. |
| |
|
| | This specific checkpoint is **Sheared-LLaMA-1.3B** fine-tuned using the SemCoT framework on the **MultiArith** dataset. |
| |
|
| | - **Paper:** [SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens](https://huggingface.co/papers/2510.24940) |
| | - **Code:** [Official GitHub Repository](https://github.com/YinhanHe123/SemCoT) |
| |
|
| | ## 🎯 Key Features |
| | - 🗣️ **Semantic Alignment**: Uses a contrastively trained sentence transformer to ensure that implicit reasoning tokens remain semantically consistent with human-readable CoT explanations. |
| | - ⚡ **Efficiency Optimization**: Introduces a lightweight implicit reasoning generator, fine-tuned via knowledge distillation, to reduce token generation time and enhance inference speed. |
| | - 🧩 **Joint Optimization**: SemCoT is the first approach to jointly optimize both token-level generation speed and semantic alignment with ground-truth reasoning. |
| |
|
| | ## 🛠️ Usage |
| | To use this model, please refer to the [official implementation on GitHub](https://github.com/YinhanHe123/SemCoT/) as it requires the SemCoT framework to handle the implicit reasoning tokens correctly. |
| |
|
| | ## Citation |
| | ```bibtex |
| | @inproceedings{he2025semcot, |
| | title={SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens}, |
| | author={He, Yinhan and Zheng, Wendy and Zhu, Yaochen and Zheng, Zaiyi and Su, Lin and Vasudevan, Sriram and Guo, Qi and Hong, Liangjie and Li, Jundong}, |
| | booktitle={39th Conference on Neural Information Processing Systems (NeurIPS 2025)}, |
| | year={2025} |
| | } |
| | ``` |
