ETCHR GRPO-10K

📖Paper | 🏠Homepage | 🤗ETCHR-FLUX.2-klein-9B Model | 🤗ETCHR SFT-400K Dataset | 🤗ETCHR GRPO-10K Dataset | 🤗DL3DV-2K Benchmark

ETCHR GRPO-10K is the GRPO training data for further enhance ETCHR's editing capaibility in assisting understanding models. It contains 10000 samples of five tasks (Fine-grained Perception, Chart Understanding, Maze Solving, Jigsaw Puzzle and Spatial Understanding). Each sample contains the image to be edited, an editing instruction, and a corresponding understanding task associated with this image for measuring the editing quality via guidance reward.

📢 News

• 🚀 [2026/05/22] We have released the training and evaluation code of ETCHR.
• 🚀 [2026/05/21] We have released the ETCHR-FLUX.2-klein-9B Model, ETCHR-SFT-400K Dataset and ETCHR GRPO-10K Dataset.

🌈 Overview

We are thrilled to introduce ETCHR (Editing To Clarify and Harness Reasoning), a novel question-conditioned, reasoning-aware image editor designed to serve as a decoupled visual reasoning assistant for Multimodal Large Language Models (MLLMs).

By decoupling the specialized image editor from the downstream understanding model, ETCHR bridges the critical bottleneck where a purely textual chain of thought fails in fine-grained focus or complex spatial transformations.

💡 Highlights

• 🔥 Decoupled & Plug-and-Play: ETCHR functions as a separate module, allowing it to assist diverse downstream MLLMs (such as Qwen3-VL-8B, Gemini-3.1-Flash-Lite, or Kimi K2.5) without requiring any task-specific fine-tuning on the understanding models themselves.
• 🔥 Naturally Reflective Pipeline: Introduces an Edit-Verify-Reason inference mechanism where the understanding model filters out noisy or flawed edits, reverting safely to the original image when verification fails.

🛠️ Usage

You can find all source images, edit instructions and qa list for Guidance Reward in GRPO-10K.parquet. See https://github.com/InternLM/ETCHR/blob/master/RL/RL.md for further details.

✒️Citation

If you find this project useful, please kindly cite:

@article{zhang2026etchr,
  title={ETCHR: Editing To Clarify and Harness Reasoning},
  author={Beichen Zhang, Yuhong Liu, Jinsong Li, Yuhang Zang, Jiaqi Wang, Dahua Lin},
  journal={arXiv preprint arXiv:2605.23897},
  year={2026}
}