Next Concept Prediction in Discrete Latent Space Leads to Stronger Language Models

📄 arXiv · 🤗 Hugging Face

This repository contains the official implementation of ConceptLM and Next-Concept-Prediction (NCP),

• Release ConceptLM models (The largest Pythia and Llama models are released)
• Release test code
• Release all models, including ablation studies, analyses, and their training curves
• Release training code
• ...

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🔍 Introduction

We propose Next Concept Prediction (NCP), a generative pretraining paradigm built on top of Next Token Prediction (NTP). Our model, ConceptLM, quantizes hidden states using Vector Quantization and constructs a concept vocabulary. It leverages both NCP and NTP to drive parameter updates and generates a concept to guide the generation of the following tokens.

Key highlights:

• Introduce a Harder LLM pre-training objective for NCP.
• Build up Concept Representation in Discrete Latent Space (Concept Vocabulary) upon LLM continuous latent space.
• Introduce a Novel architecture ConceptLM integrates NCP and NTP.

🖼️ Overview

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📁 Code Structure

We implement the generate function for the Llama model. So that the details of ConceptLM_Llama differ from the Pythia and GPT-2 implementations.

.
├── figures/        
├── lm_eval/   
  ├── lm_eval/   
      ├── lm_eval_files/
      ├── ConceptLM_arc/
          ├── ConceptLM_GPT2/
          ├── ConceptLM_Pythia/
          └── ConceptLM_Llama/
      └── README.md
└── README.md

📁 Quick Start

To reproduce our results, you can download our model first, then run:

git clone https://github.com/LUMIA-Group/ConceptLM

cd ./lm_eval

pip install -e .

pip install transformers==4.51, vector_quantize_pytorch, flash_attn

# download our models

bash run_lm_eval.sh

📖 Citation

If you have any questions or are interested in our work, please feel free to contact us at liuyl03181@gmail.com.

If you find this work useful, please consider citing our paper:

@article{conceptlm2026,
  title   = {Next Concept Prediction in Discrete Latent Space Leads to Stronger Language Models},
  author  = {Yuliang Liu and Yunchong Song and Yixuan Wang and Kewen Ge and Alex Lamb and Qipeng Guo and Kai Chen and Bowen Zhou and Zhouhan Lin},
  journal = {Arxiv},
  year    = {2026}
}