README.md

metadata

license: mit
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - visual-reasoning
  - reinforcement-learning
  - code-generation
  - multimodal

Learning Only with Images: Visual Reinforcement Learning with Reasoning, Rendering, and Visual Feedback

This repository contains the RRVF-Chart Model, a checkpoint of the model presented in the paper Learning Only with Images: Visual Reinforcement Learning with Reasoning, Rendering, and Visual Feedback.

The official code for the RRVF framework is available on GitHub: syficy/RRVF-framework

Abstract

Multimodal Large Language Models (MLLMs) exhibit impressive performance across various visual tasks. Subsequent investigations into enhancing their visual reasoning abilities have significantly expanded their performance envelope. However, a critical bottleneck in the advancement of MLLMs toward deep visual reasoning is their heavy reliance on curated image-text supervision. To solve this problem, we introduce a novel framework, Reasoning-Rendering-Visual-Feedback'' (RRVF), that enables MLLMs to learn complex visual reasoning from only raw images. This framework builds on the Asymmetry of Verification'' principle, i.e., verifying the rendered output against the source image is substantially easier than performing deep visual reasoning to generate a faithful, structured representation such as code. We demonstrate that this relative ease provides an ideal reward signal for optimization via Reinforcement Learning (RL), thereby reducing reliance on image-text supervision. RRVF implements a closed-loop iterative process encompassing reasoning, rendering, and visual feedback components, enabling the model to perform complex reasoning, including self-correction through multi-turn interactions. This process is optimized end-to-end using the GRPO algorithm. Extensive evaluations are conducted on image-to-code generation across two diverse domains: data charts and web interfaces. The RRVF-trained model not only outperforms existing similarly sized open-source MLLMs and supervised fine-tuning baselines but also exhibits superior generalization. Notably, the model outperforms the more advanced MLLM used to generate visual feedback during training.

Framework Overview

RRVF is a training framework that enhances the visual reasoning capabilities of MLLMs using purely visual signals. Its core is a closed-loop system comprising three key components: an iterative visual reasoner, a visual feedback mechanism, and a final visual judge.

Figure: The RRVF framework.

Iterative Visual Reasoning

The reasoning process is iterative. The model receives an image and produces a response containing internal thoughts (in <think> tags) and a specific action (in a <tool_call> tag). After the tool executes the code, the visual feedback is appended to the conversation history to inform the model's next turn, until it generates a final solution (in an <answer> tag).

Visual Feedback Mechanism

This is the key to guiding the model's learning.

1. Rendering: The model-generated code is executed by a domain-specific engine (e.g., Matplotlib for charts, Playwright for web pages) to render an image.
2. Comparison & Feedback: A more powerful "teacher" MLLM compares the rendered image to the original, articulating the visual discrepancies (e.g., color, layout, missing elements) in natural language. This descriptive feedback provides actionable guidance for the model.

Reinforcement Learning Optimization

The entire closed-loop process is formulated as a reinforcement learning task and optimized with the GRPO algorithm. We designed a hybrid reward function to guide the learning:

• Visual Similarity Reward (R_vision): Provided by the visual judge, this quantifies the fidelity between the final rendered image and the original input.
• Format Correctness Reward (R_format): Penalizes improper output formatting and non-executable code.
• Tool-Use Reward (R_tool): Encourages exploration and iterative refinement by rewarding successful tool calls.

Main Results

Chart-to-Code Task

Results on the ChartMimic test set:

Model	Exec rate	Text	Layout	Type	Color	GPT-4o score	Overall
Closed-Source MLLMs
(2024/02) Gemini-1.0-Pro-Vision	68.2*	52.6*	64.2*	51.3*	47.1*	53.3*	53.6*
(2024/11) GPT-4o-2024-11-20	90.00	66.55	79.31	71.83	60.84	82.50	76.06
(2025/04) OpenAI o3	90.17	74.17	80.58	71.37	63.74	86.45	79.46
(2025/05) Claude-4-Sonnet	91.83	68.87	82.43	67.13	57.59	85.46	77.23
(2025/06) Gemini-2.5-Pro	93.33	84.95	83.37	75.05	66.90	90.58	84.07
Open-Source MLLMs
(2025/02) Qwen2.5-VL-72B-Instruct	83.83	34.44	61.71	45.49	35.12	50.41	47.30
(2024/03) DeepSeek-VL-7B	41.3*	15.3*	26.6*	19.7*	14.5*	20.4*	19.7*
(2025/02) LLaVA-OneVision-7B	17.28	7.97	13.55	9.15	7.36	10.01	9.76
(2025/02) Qwen2.5-VL-7B-Instruct	68.83	30.01	55.79	36.50	26.91	39.04	38.17
(2025/04) InternVL3-8B	71.67	45.03	57.89	45.87	38.88	54.91	50.91
---	---	---	---	---	---	---	---
SFT [with text labels]	69.00	56.97	63.60	60.53	51.89	62.09	60.17
Δ (vs Qwen2.5-VL-7B-Instruct)	+0.17	+26.96	+7.81	+24.03	+24.98	+23.05	+22.00
---	---	---	---	---	---	---	---
RRVF (Ours) [without text labels]	97.83	62.47	80.97	53.56	46.41	67.87	64.36
Δ (vs Qwen2.5-VL-7B-Instruct)	+29.00	+32.46	+25.18	+17.06	+19.50	+28.83	+26.19

note： Performance comparison on the ChartMimic benchmark. We report the metrics from the original ChartMimic benchmark. The best and second-best results among open-source models under 10B parameters are bolded and underlined, respectively. Results marked with * are reported by the original benchmark.

Results on Plot2Code (Zero-Shot):

Model	Exec Rate	Text	GPT-4o Score	Textpass	GPT-4o Scorepass
Closed-Source MLLMs
(2023/09) GPT-4V	84.1*	48.53*	5.45*	57.7*	6.48*
(2024/02) Gemini-1.0-Pro-Vision	68.2*	36.56*	3.45*	53.6*	5.06*
(2024/06) Claude-3-Sonnet	75.8*	35.40*	4.08*	46.7*	5.38*
(2024/11) GPT-4o-2024-11-20	90.15	48.91	6.09	54.25	6.76
(2025/04) OpenAI o3	87.12	57.65	6.70	66.17	7.69
(2025/05) Claude-4-Sonnet	92.42	56.86	6.16	61.52	6.76
(2025/06) Gemini-2.5-Pro	87.88	71.70	7.65	81.59	8.71
Open-Source MLLMs
(2025/02) Qwen2.5-VL-72B-Instruct	83.33	56.74	5.79	68.09	6.95
(2024/03) Mini-Gemini-8x7B-HD	73.5*	29.91*	2.84*	40.7*	3.87*
(2025/02) LLaVA-OneVision-7B	84.09	26.72	2.75	31.78	3.27
(2025/02) Qwen2.5-VL-7B-Instruct	70.46	35.80	3.40	50.81	4.82
(2025/04) InternVL3-8B	76.52	30.67	3.25	40.08	4.25
---	---	---	---	---	---
SFT [with text labels, ChartMimic trained]	49.24	21.63	2.47	43.93	5.02
Δ (vs Qwen2.5-VL-7B-Instruct)	-21.22	-14.17	-0.93	-	-
---	---	---	---	---	---
RRVF (Ours) [without text labels]	96.21	39.89	4.44	41.46	4.61
Δ (vs Qwen2.5-VL-7B-Instruct)	+25.75	+4.09	+1.04	-	-

note:
Performance comparison on the Plot2Code benchmark. The best and second-best results on the primary metrics (Exec Rate, Text, GPT-4o Score) among open-source models under 10B parameters are bolded and underlined, respectively. Results marked with * are reported by the original benchmark.

Web-to-Code Task

Results on the WebSight test set:

Model	CLIP Score	GPT Score
Closed-Source MLLMs
GPT-4o-2024-11-20	88.94	94.55
OpenAI o3	91.58	96.49
Claude-4-Sonnet	92.30	96.46
Gemini-2.5-Pro	77.83	75.88
Open-Source MLLMs
LLaVA-OneVision-7B	79.74	72.61
Qwen2.5-VL-7B-Instruct	83.50	84.17
InternVL3-8B	84.17	85.54
---	---	---
RRVF (Ours)	88.29	91.50

note: Performance comparison on the WebSight benchmark for web interface generation. The best results among open-source models under 10B parameters are bolded.

Usage

You can use this model with the transformers library, which automatically handles the visual and language components.

from transformers import pipeline
from PIL import Image

# Load the model and processor
# This model card is for a chart-to-code model, e.g., "chenzju/rrvf_chartmimic"
model_id = "chenzju/rrvf_chartmimic" 
pipe = pipeline("image-to-text", model=model_id)

# Example image (replace with your chart image)
# You would load an image of a chart here.
# For example, using PIL:
# from PIL import Image
# image = Image.open("path/to/your/chart_image.png")
# Let's create a dummy image for demonstration:
image = Image.new('RGB', (224, 224), color = 'red')

# Define your prompt (instruction for code generation)
prompt = "Generate the Python code to recreate this chart using Matplotlib."

# Run inference
result = pipe(image, prompt)

# Print the generated code
print(result)

Citation

If you use our work in your research, please cite our paper:

@misc{chen2025learningimagesvisualreinforcement,
      title={Learning Only with Images: Visual Reinforcement Learning with Reasoning, Rendering, and Visual Feedback}, 
      author={Yang Chen and Yufan Shen and Wenxuan Huang and Sheng Zhou and Qunshu Lin and Xinyu Cai and Zhi Yu and Jiajun Bu and Botian Shi and Yu Qiao},
      year={2025},
      eprint={2507.20766},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.20766}, 
}

Acknowledgements

• We thank the Verl, DeepEyes framework.
• We thank the creators of the ChartMimic, Plot2Code, and WebSight datasets.
• We thank the VlmEvalkit team.