jonathanhe123/SemCoT-mistral-1.1b-coin_flip

SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens

Paper | Code

🚀 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 performance.

This specific checkpoint is a fine-tuned version of optimum/mistral-1.1b-testing using the SemCoT framework on the skrishna/coin_flip dataset.

🎯 Key Features

• 🗣️ Semantic Alignment: Uses a contrastively trained sentence transformer to ensure that implicit reasoning remains 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 that enhances CoT efficiency by jointly optimizing token-level generation speed and preserving semantic alignment with ground-truth reasoning.

Usage

Please refer to the official GitHub repository for detailed instructions on how to load and use the model for inference and training.

Citation

@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}
}