base_model: zai-org/Glyph
library_name: transformers
license: other
pipeline_tag: image-text-to-text
tags:
- llama-factory
- full
- generated_from_trainer
- vision-language-model
- reasoning
model-index:
- name: vtc-r1-glyph
results: []
VTC-R1-Glyph
VTC-R1 (Vision-Text Compression for Efficient Long-Context Reasoning) is an efficient reasoning paradigm that integrates vision-text compression into the reasoning process. This repository contains the fine-tuned version of zai-org/Glyph (based on GLM-4V) using this paradigm.
- Paper: VTC-R1: Vision-Text Compression for Efficient Long-Context Reasoning
- Repository: https://github.com/w-yibo/VTC-R1
Model Description
VTC-R1 addresses efficiency bottlenecks in long-context reasoning for Vision-Language Models (VLMs). Instead of processing lengthy textual traces, VTC-R1 renders intermediate reasoning segments into compact images, which are iteratively fed back into the model as "optical memory."
Key features:
- Efficiency: Achieves 3.4x token compression and 2.7x speedup in end-to-end latency.
- Performance: Outperforms standard long-context reasoning on benchmarks like MATH500, AIME25, AMC23, and GPQA-D.
- Scalability: Integrates vision-text compression directly into the reasoning process without needing external compression models.
Setup & Inference
Installation
To use this model, install the required dependencies:
apt-get install poppler-utils # or conda install -c conda-forge poppler
pip install torch==2.6.0
pip install transformers==4.57.1
pip install reportlab
pip install pdf2image
Inference
You can run the inference code provided in the official repository to generate VTC-R1 style reasoning:
python inference.py # replace your model path in the script
Training Procedure
The model was fine-tuned using LLaMA-Factory on a dataset derived from OpenR1-Math-220K.
Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Citation
If you find this work useful, please cite:
@misc{wang2026vtcr1visiontextcompressionefficient,
title={VTC-R1: Vision-Text Compression for Efficient Long-Context Reasoning},
author={Yibo Wang and Yongcheng Jing and Shunyu Liu and Hao Guan and Rong-cheng Tu and Chengyu Wang and Jun Huang and Dacheng Tao},
year={2026},
eprint={2601.22069},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2601.22069},
}
Framework Versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.22.1