---
title: VLAC-Cut
canonical_url: "https://www.modelscope.ai/models/InternRobotics/VLAC-Cut"
md_url: "https://www.modelscope.ai/models/InternRobotics/VLAC-Cut.md"
repository: InternRobotics/VLAC-Cut
last_updated: 2026-07-16
license: other
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - qwen3_vl_moe
architectures:
  - Qwen3VLMoeForConditionalGeneration
base_model:
  - Qwen/Qwen3-VL-30B-A3B-Instruct
base_model_relation: finetune
parameters: 31.1B
tensor_type:
  - BF16
library_name:
  - transformer
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 118
stars: 0
tags:
  - robotics
  - embodied-ai
  - video-understanding
  - progress-estimation
  - reward-modeling
  - qwen3-vl
---

# VLAC-Cut

> VLAC-Cut - An open-source model by InternRobotics on ModelScope. VLAC-Cut: Video Progress Estimation for Process-Level Robot Rollout Segmentation

InternRobotics/VLAC-Cut is a 31.1B-parameter image-text-to-text model on ModelScope. licensed under other. derived from Qwen/Qwen3-VL-30B-A3B-Instruct.

- **Repository**: InternRobotics/VLAC-Cut
- **License**: other
- **Tasks**: image-text-to-text
- **Parameters**: 31.1B
- **Base model**: Qwen/Qwen3-VL-30B-A3B-Instruct
- **Tags**: robotics, embodied-ai, video-understanding, progress-estimation, reward-modeling, qwen3-vl
- **Downloads**: 118
- **Stars**: 0
- **Last updated**: 2026-07-16

Source: https://www.modelscope.ai/models/InternRobotics/VLAC-Cut

---

# VLAC-Cut: Video Progress Estimation for Process-Level Robot Rollout Segmentation

<div align="center">

[Paper](https://arxiv.org/abs/2607.09776) ·
[Code](https://github.com/InternRobotics/VLAC-cut) ·
[Model](https://huggingface.co/InternRobotics/VLAC-Cut) ·
[Benchmark](https://huggingface.co/datasets/InternRobotics/VLAC-Cut-Benchmark)

</div>

## Overview

**VLAC-Cut** is a process-level multimodal trajectory critic for robot post-training data curation. Given a natural-language task instruction, an optional task plan, and a robot rollout video, VLAC-Cut estimates signed task progress over time and identifies temporal segments associated with task advancement or degradation.

Unlike methods that assume task progress increases monotonically over time, VLAC-Cut models non-monotonic execution dynamics, including advancement, stagnation, regression, and recovery. This formulation supports process-level analysis of partial completion, temporary failure, subsequent recovery, and rollout segmentation for post-training data selection.

This Hugging Face repository contains the VLAC-Cut model weights and loading assets. The official inference examples and evaluation code are maintained in the GitHub repository.

## Highlights

* **Video-level temporal reasoning:** Analyzes robot execution videos rather than isolated images or image pairs and identifies temporal segments associated with task advancement or degradation.
* **Non-monotonic progress estimation:** Captures advancement, stagnation, regression, and recovery without imposing a monotonically increasing progress assumption.
* **Zero-shot generalization:** Generalizes across manipulation tasks, scenes, object configurations, and camera viewpoints.
* **Flexible temporal resolution:** Supports configurable video sampling frequencies for both coarse- and fine-grained progress estimation.

## Model Overview

| Property | Description |
|---|---|
| Base model | `Qwen/Qwen3-VL-30B-A3B-Instruct` |
| Input | Task instruction, optional task plan, and sampled video frames |
| Output | Timestamped task-progress estimates |
| Sampling rate | `2 Hz`-`20 Hz` |
| Default sampling rate | `2.0 Hz` |

## Load with Transformers

```python
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "InternRobotics/VLAC-Cut"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)
model.eval()
```

## Quick Start

Run progress inference on a local video using the GitHub code:

```bash
git clone https://github.com/InternRobotics/VLAC-cut
cd VLAC-cut

python scripts/run_example.py \
  --model-path InternRobotics/VLAC-Cut \
  --video-path <path-to-video.mp4> \
  --task-instruction "<natural-language task instruction>" \
  --task-plan $'<optional step-by-step task plan>' \
  --output-jsonl <path-to-output.jsonl>
```

Render a prediction JSONL file as an annotated video:

```bash
python scripts/utils/render_prediction_video.py \
  --input-jsonl <path-to-output.jsonl> \
  --output-video <path-to-preview.mp4>
```

## Citation

Please cite the following paper when using VLAC-Cut, the released model, or the Video Progress Benchmark:

```bibtex
@misc{zhai2026helphumanefficientlargescalerobot,
      title={HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation}, 
      author={Shaopeng Zhai and Qi Zhang and Tianyi Zhang and Haoran Zhang and Fuxian Huang and Zhanhui Lin and Zijun Xu and Weinan Zhang},
      year={2026},
      eprint={2607.09776},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2607.09776}, 
}
```

## License

The model weights and third-party training data may be subject to additional licenses or terms of use. The source code in the GitHub repository is released under the MIT License.