[recipe] feat: Add InfiGUI-G1 recipe for MLLM GUI grounding

recipe/infigui-g1/README.md

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+# Recipe for InfiGUI-G1
+
+This directory contains the official implementation for the paper [InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy Optimization](https://arxiv.org/abs/2508.05731).
+
+This work introduces Adaptive Exploration Policy Optimization (AEPO), a policy optimization framework designed to enhance GUI grounding in Multimodal Large Language Models (MLLMs). AEPO improves exploration efficiency by employing a multi-answer generation strategy and a theoretically grounded Adaptive Exploration Reward (AER) function. This approach effectively addresses the challenge of semantic alignment in complex GUI grounding tasks.
+
+We provide training scripts for both 3B and 7B models, configured for a single machine with 8 GPUs by default.
+
+## Environment Setup
+
+Please follow the main environment setup guide for `verl`.
+
+The provided scripts use the following Docker image: `verlai/verl:app-verl0.5-transformers4.55.4-sglang0.4.10.post2-mcore0.13.0-te2.2`
+
+## Data Preparation
+
+Before starting the training, you need to download the example dataset. This dataset is a filtered version of [omniact](https://huggingface.co/datasets/Writer/omniact), containing only grounding tasks and excluding easy samples.
+
+The data is hosted on the Hugging Face. You can download it using the `huggingface-cli`:
+
+```bash
+huggingface-cli download --repo-type dataset --resume-download InfiX-ai/omniact_grounding_filtered --local-dir data/omniact_grounding_filtered
+```
+
+This command will download the training and validation parquet files into the `data/omniact_grounding_filtered` directory, which is the default path used by the scripts.
+
+## Training
+
+We provide scripts to train the 3B and 7B models. Please run them from the root directory of `verl`.
+
+-   **Train the 3B model:**
+
+    ```bash
+    bash recipe/infigui-g1/run_3b.sh
+    ```
+
+-   **Train the 7B model:**
+
+    ```bash
+    bash recipe/infigui-g1/run_7b.sh
+    ```
+
+## Using Custom Data
+
+If you wish to train on your own dataset, please format your data to match the structure of the example files located in `data/omniact_grounding_filtered`.
+
+Once your data is ready, you need to update the data path arguments in the training script.
+
+In `run_3b.sh` or `run_7b.sh`, modify the following lines:
+
+```bash
+    data.train_files=./path/to/your/train_data.parquet \
+    data.val_files=./path/to/your/val_data.parquet \
+```
+
+Replace the paths with the location of your custom data files.