internlm/VC-RewardBench

---
license: apache-2.0
---

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<img src="https://cdn-uploads.huggingface.co/production/uploads/63859cf3b2906edaf83af9f0/gcuIXKMoDd-nQoPrynVQF.png" width="50%">
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# Visual-ERM

Visual-ERM is a **multimodal generative reward model** for **vision-to-code** tasks.
It evaluates outputs directly in the **rendered visual space** and produces **fine-grained**, **interpretable**, and **task-agnostic** discrepancy feedback for structured visual reconstruction.

<p align="center">
<a href="https://arxiv.org/abs/2603.13224">📄 Paper</a> |
<a href="https://github.com/InternLM/Visual-ERM">💻 GitHub</a> |
<a href="https://huggingface.co/datasets/internlm/VC-RewardBench">📊 VC-RewardBench</a>
</p>

## Model Overview

Existing rewards for vision-to-code usually fall into two categories:

1. **Text-based rewards** such as edit distance or TEDS, which ignore important visual cues like layout, spacing, alignment, and style.
2. **Vision embedding rewards** such as DINO similarity, which are often coarse-grained and can be vulnerable to reward hacking.

Visual-ERM addresses this by directly comparing:

- the **ground-truth image**, and
- the **rendered image** produced from a model prediction,

and then generating **structured discrepancy annotations** that can be converted into reward signals or used for reflection-based refinement.

## What this model does

Visual-ERM is designed to judge whether a predicted result is **visually equivalent** to the target.

Given a pair of images, it can identify discrepancies such as:

- **category**
- **severity**
- **location**
- **description**

This makes Visual-ERM useful not only as a reward model for RL, but also as a **visual critic** for test-time reflection and revision.

## Supported Tasks

Visual-ERM is designed for structured visual reconstruction tasks, including:

- **Chart-to-Code**
- **Table-to-Markdown**
- **SVG-to-Code**

## Key Features

- **Visual-space reward modeling**
Evaluates predictions in rendered visual space instead of relying only on text matching or coarse embedding similarity.

- **Fine-grained and interpretable feedback**
Produces structured discrepancy annotations rather than a single black-box score.

- **Task-agnostic reward supervision**
A unified reward model that generalizes across multiple vision-to-code tasks.

- **Useful for both training and inference**
Can be used as a reward model in RL and as a visual critic during test-time refinement.

## VC-RewardBench

We also release **VisualCritic-RewardBench (VC-RewardBench)**, a benchmark for evaluating fine-grained image-to-image discrepancy judgment on structured visual data.

### Benchmark Features

- Covers **charts**, **tables**, and **SVGs**
- Contains **1,335** carefully curated instances
- Each instance includes:
- a ground-truth image
- a corrupted / rendered counterpart
- fine-grained discrepancy annotations

Dataset link:
https://huggingface.co/datasets/internlm/VC-RewardBench

## How to Use

Visual-ERM is fine-tuned from **Qwen/Qwen3-VL-8B-Instruct** and follows the same multimodal interface.

### Input

Visual-ERM takes as input:

- a **reference / ground-truth image**
- a **rendered prediction image**
- a **prompt** asking the model to identify fine-grained visual discrepancies

### Output

The model outputs structured discrepancy annotations, which can then be:

- converted into a scalar reward for RL
- used as feedback for reflection-and-revision
- evaluated directly on VC-RewardBench

A typical output format is:

```json
{
"errors": [
{
"category": "structure_error",
"severity": 3,
"location": "legend area",
"description": "The legend is placed outside the plot area in the prediction."
},
{
"category": "style_error",
"severity": 2,
"location": "bar colors",
"description": "The colors differ from those in the reference image."
}
]
}
```

### Inference / Evaluation / RL

For full inference scripts, RL training pipelines, evaluation code, and prompt templates, please refer to the official repository:

https://github.com/InternLM/Visual-ERM

## Intended Use

Visual-ERM is intended for:

- **reward modeling** in vision-to-code RL pipelines
- **visual discrepancy judgment** between target and predicted renderings
- **reflection-based refinement** at inference time
- **research on visual reward modeling** and multimodal RL

## Citation

If you find this model useful, please consider citing:

```bibtex
@article{liu2026visual,
title={Visual-ERM: Reward Modeling for Visual Equivalence},
author={Liu, Ziyu and Ding, Shengyuan and Fang, Xinyu and Dai, Xuanlang and Yang, Penghui and Liang, Jianze and Wang, Jiaqi and Chen, Kai and Lin, Dahua and Zang, Yuhang},
journal={arXiv preprint arXiv:2603.13224},
year={2026}
}
```

## Contact

If you are interested in **visual reward modeling**, **vision-to-code**, or **reinforcement learning for multimodal models**, feel free to reach out.