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README.md

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 ## 📢 Updates
+- [2025-12-10] We released training code, sampling code, SQL schema, and related Docker configurations.
+- [2025-11-30] We released ENV code and Docker setup.
+- [2025-10-30] We released checkpoint and inference code.
 - [2025-09-30] We released our [paper](https://arxiv.org/abs/2509.23866) and [project page](https://computer-use-agents.github.io/dart-gui). Check it out!
 
 ## 🔨 TODO
-- [ ] Release the model checkpoint for DART-GUI-7B.
-- [ ] Add the training code and pipeline.
+- [ ] Polish the codebase.
+- [ ] Merge with the latest verl version.
+- [x] Release the model checkpoint for DART-GUI-7B.
+- [x] Add the training code and pipeline.
+- [x] Release checkpoint and inference code.
+- [x] Release ENV code and Docker setup.
+- [x] Release training code and sampling code.
+- [x] Release SQL schema and related Docker configurations.
+
+## 🚀 Quick Start
+
+This guide provides instructions for setting up the DART-GUI environment, including Docker container initialization, database schema configuration, and execution scripts for sampling and training.
+
+### 1. Preparation
+
+#### Download Docker Images
+
+Pull the required images for the GUI agent and the database.
+
+```bash
+# DART-GUI Image
+docker pull crpi-iwtwdoj3ikoon38c.cn-beijing.personal.cr.aliyuncs.com/pengxiangli1999/dart-gui:v0
+
+# MySQL Image
+docker pull mysql:8.0.44-debian
+```
+
+#### Prepare Model Checkpoints
+
+Download the `UI-TARS-1.5-7B` model using the HuggingFace CLI. Replace `<your local path>` with your actual directory.
+
+```bash
+huggingface-cli download ByteDance-Seed/UI-TARS-1.5-7B --local-dir <your local path>
+```
+
+### 2. Docker Initialization
+
+Initialize the containers on your **GPU Machine**. Ensure you replace specific paths (like MySQL volume) with your local paths.
+
+#### Rollouter Container
+
+Used for the rollout service.
+
+```bash
+docker run -dit \
+  --name rollouter \
+  --gpus all \
+  -p 6008:6008 \
+  -p 8881:8881 \
+  -p 15959:15959 \
+  --shm-size=200g \
+  crpi-iwtwdoj3ikoon38c.cn-beijing.personal.cr.aliyuncs.com/pengxiangli1999/dart-gui:v0
+```
+
+#### Trainer Container
+
+Used for model training.
+
+```bash
+docker run -dit \
+  --name trainer \
+  --gpus all \
+  -p 6009:6008 \
+  -p 8882:8881 \
+  -p 15960:15959 \
+  --shm-size=1500g \
+  crpi-iwtwdoj3ikoon38c.cn-beijing.personal.cr.aliyuncs.com/pengxiangli1999/dart-gui:v0
+```
+
+#### MySQL Container
+
+Database server for tracking runs and checkpoints. Replace `<your sql default path>` with your local storage path.
+
+```bash
+docker run -dit \
+  --name mysql-server \
+  -p 3306:3306 \
+  -e MYSQL_ROOT_PASSWORD=admin \
+  -v <your sql default path>:/var/lib/mysql \
+  mysql:8.0.44-debian
+```
+
+### 3. Database Configuration
+
+Connect to your MySQL container and initialize the tables using the SQL below.
+
+**Database Credentials:**
+- **User:** root
+- **Password:** admin
+- **Port:** 3306
+
+#### SQL Schema
+
+```sql
+--
+-- Table structure for table `rollout_run`
+--
+
+DROP TABLE IF EXISTS `rollout_run`;
+/*!40101 SET @saved_cs_client     = @@character_set_client */;
+/*!50503 SET character_set_client = utf8mb4 */;
+CREATE TABLE `rollout_run` (
+  `id` bigint NOT NULL AUTO_INCREMENT,
+  `run_id` varchar(191) CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci DEFAULT NULL,
+  `trajectory_id` varchar(191) COLLATE utf8mb4_unicode_ci NOT NULL,
+  `task_id` varchar(191) COLLATE utf8mb4_unicode_ci NOT NULL,
+  `trace_id` varchar(191) COLLATE utf8mb4_unicode_ci NOT NULL,
+  `split_dir` varchar(512) CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci DEFAULT NULL,
+  `reward` double DEFAULT NULL,
+  `num_chunks` int DEFAULT NULL,
+  `used` int NOT NULL DEFAULT '0',
+  `model_version` varchar(512) CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci DEFAULT NULL,
+  `instruction` varchar(1024) CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci DEFAULT NULL,
+  `create_at` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
+  PRIMARY KEY (`id`),
+  UNIQUE KEY `uq_rollout_run_id` (`id`),
+  UNIQUE KEY `uk_rollout_run_traj_run` (`trajectory_id`,`run_id`),
+  KEY `idx_rollout_run_task` (`task_id`)
+) ENGINE=InnoDB AUTO_INCREMENT=1319846 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
+/*!40101 SET character_set_client = @saved_cs_client */;
+
+--
+-- Table structure for table `checkpoint`
+--
+
+DROP TABLE IF EXISTS `checkpoint`;
+/*!40101 SET @saved_cs_client     = @@character_set_client */;
+/*!50503 SET character_set_client = utf8mb4 */;
+CREATE TABLE `checkpoint` (
+  `id` bigint NOT NULL AUTO_INCREMENT COMMENT 'Primary Key ID',
+  `name` varchar(50) NOT NULL DEFAULT '' COMMENT 'Checkpoint Name (Unique English Identifier)',
+  `version` varchar(50) NOT NULL COMMENT 'Version Number (Semantic Versioning, e.g., v1.0.0)',
+  `run_id` varchar(191) NOT NULL DEFAULT '',
+  `status` varchar(20) NOT NULL DEFAULT 'PENDING' COMMENT 'Status: PENDING|RUNNING|COMPLETED|FAILED|DEPRECATED',
+  `path` varchar(255) NOT NULL COMMENT 'Storage Path (e.g., s3://bucket/path/checkpoint.ckpt)',
+  `source` varchar(50) DEFAULT NULL COMMENT 'Source (e.g., User Upload/Training Generated/System Migration)',
+  `operator` varchar(50) DEFAULT NULL COMMENT 'Operator (User ID or System Account)',
+  `remark` varchar(1024) DEFAULT NULL COMMENT 'Remark (Free text format)',
+  `config_yaml` text COMMENT 'Full Deployment Config (Encrypted Storage)',
+  `created_at` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT 'Created At',
+  `updated_at` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT 'Last Updated At',
+  `deleted_at` timestamp NULL DEFAULT NULL COMMENT 'Soft Delete Time',
+  `started_at` timestamp NULL DEFAULT NULL COMMENT 'Started At',
+  `finished_at` timestamp NULL DEFAULT NULL COMMENT 'Finished At',
+  PRIMARY KEY (`id`),
+  KEY `idx_status` (`status`),
+  KEY `idx_created_at` (`created_at`),
+  KEY `idx_updated_at` (`updated_at`)
+) ENGINE=InnoDB AUTO_INCREMENT=3117 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci COMMENT='Model Checkpoint Table (Records training checkpoints and deployment versions)';
+/*!40101 SET character_set_client = @saved_cs_client */;
+```
+
+### 4. Environment Setup (CPU Machine)
+
+Please follow the instructions in the repository below to initialize the environment on your **CPU machine**:
+
+- **Repository:** [GUI-Docker-Env](https://github.com/Computer-use-agents/GUI-Docker-Env.git)
+
+### 5. Execution
+
+#### Step 1: Start Rollouter (GPU Machine)
+
+Inside the `rollouter` docker container:
+
+```bash
+sh model_service.sh
+```
+
+#### Step 2: Start Agent Runner (CPU Machine)
+
+Inside the configured CPU environment:
+
+```bash
+sh run.sh
+```
+
+#### Step 3: Start Training (GPU Machine)
+
+Once the links are set up and data is flowing, start the training process inside the `trainer` docker container:
+
+```bash
+sh examples/osworld/async/run_trainer_debug_w_rollout_stepwise_train_pt.sh
+```
 
 ## 🤗 Team
 ### Core Contributors
@@ -96,4 +280,4 @@ If you find our paper and code useful in your research, please consider giving a
 
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