Improve model card: Add pipeline tag, library name, and comprehensive details from GitHub

README.md

@@ -1,41 +1,31 @@
 ---
-license: apache-2.0
-language:
-- en
+base_model:
+- Qwen/Qwen2-VL-2B-Instruct
 datasets:
 - tanhuajie2001/Reason-RFT-CoT-Dataset
+language:
+- en
+license: apache-2.0
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2-VL-2B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
 <div align="center">
 <img src="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/logo.png" width="500"/>
 </div>
 
-# 🤗 Reason-RFT CoT Dateset
-*The model checkpoints in our project "Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning"*.
-
+# Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning of Vision Language Models
 
 <p align="center">
-    </a>&nbsp&nbsp⭐️ <a href="https://tanhuajie.github.io/ReasonRFT/">Project</a></a>&nbsp&nbsp │ &nbsp&nbsp🌎 <a href="https://github.com/tanhuajie/Reason-RFT">Github</a>&nbsp&nbsp │ &nbsp&nbsp🔥 <a href="https://huggingface.co/datasets/tanhuajie2001/Reason-RFT-CoT-Dataset">Dataset</a>&nbsp&nbsp │ &nbsp&nbsp📑 <a href="https://arxiv.org/abs/2503.20752">ArXiv</a>&nbsp&nbsp │ &nbsp&nbsp💬 <a href="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/wechat.png">WeChat</a>
+    ⭐️ <a href="https://tanhuajie.github.io/ReasonRFT/">Project</a> │ 🌎 <a href="https://github.com/tanhuajie/Reason-RFT">Github</a> │ 🔥 <a href="https://huggingface.co/datasets/tanhuajie2001/Reason-RFT-CoT-Dataset">Dataset</a> │ 📑 <a href="https://arxiv.org/abs/2503.20752">Paper</a> │ 💬 <a href="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/wechat.png">WeChat</a>
 </p>
 
 <p align="center">
-</a>&nbsp&nbsp🤖 <a href="https://github.com/FlagOpen/RoboBrain/">RoboBrain</a>: Aim to Explore ReasonRFT Paradigm to Enhance RoboBrain's Embodied Reasoning Capabilities.
+🤖 <a href="https://github.com/FlagOpen/RoboBrain/">RoboBrain</a>: Aim to Explore ReasonRFT Paradigm to Enhance RoboBrain's Embodied Reasoning Capabilities.
 </p>
 
-## ♣️ Model List
-
-| Tasks                  | Reason-RFT-Zero-2B        | Reason-RFT-Zero-7B       | Reason-RFT-2B        | Reason-RFT-7B             |
-|------------------------|---------------------------|---------------------|---------------------------|---------------------------|
-| Visual Counting        | [🤗VC-GRPO-Zero-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Visual-Counting-Qwen2-VL-2B) | [🤗VC-GRPO-Zero-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Visual-Counting-Qwen2-VL-7B) | [🤗VC-GRPO-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Visual-Counting-Qwen2-VL-2B) | [🤗VC-GRPO-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Visual-Counting-Qwen2-VL-7B) |
-| Structure Perception   | [🤗SP-GRPO-Zero-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Structure-Perception-Qwen2-VL-2B) | [🤗SP-GRPO-Zero-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Structure-Perception-Qwen2-VL-7B) | [🤗SP-GRPO-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Structure-Perception-Qwen2-VL-2B) | [🤗SP-GRPO-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Structure-Perception-Qwen2-VL-7B) |
-| Spatial Transformation | [🤗ST-GRPO-Zero-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Spatial-Transformation-Qwen2-VL-2B) | [🤗ST-GRPO-Zero-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Spatial-Transformation-Qwen2-VL-7B) | [🤗ST-GRPO-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Spatial-Transformation-Qwen2-VL-2B) | [🤗ST-GRPO-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Spatial-Transformation-Qwen2-VL-7B) | 
-| ***Embodied Tasks***   | 🤖 *Stay Turned*   | 🤖 *Stay Turned*   | 🤖 *Stay Turned*   | 🤖 *Stay Turned*  | 
-
-
 ## 🔥 Overview
 Visual reasoning abilities play a crucial role in understanding complex multimodal data, advancing both domain-specific applications and artificial general intelligence (AGI).
 Existing methods improve VLM reasoning via Chain-of-Thought (CoT) supervised fine-tuning, using meticulously annotated training data to enhance visual reasoning capabilities.
@@ -52,18 +42,358 @@ Experimental results demonstrate Reasoning-RFT's three key advantages: **(1) Per
 <img src="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/overview.png" />
 </div>
 
-## 🗞️ News
+## <a id="RoadMap"> 🎯 RoadMap</a>
+
+- **`Support different VLMs`**: [RoboBrain](https://github.com/FlagOpen/RoboBrain/), [Qwen2-VL series](https://github.com/QwenLM/Qwen2.5-VL/), [Llava-VL series](https://github.com/LLaVA-VL/LLaVA-NeXT).
+    - Explore an efficient training paradigm to enhance [RoboBrain](https://github.com/FlagOpen/RoboBrain/)'s embodied reasoning capabilities.
+- **`Support General Visual Reasoning Tasks`**: 
+    - Data download and preparation: Please refer to [General Visual Reasoning Tasks](#Preparation).
+    - Training and evaluating for **Visual Counting**: Please refer to [Visual Counting Section](#GeneralVisualTasks).
+    - Training and evaluating for **Struction Perception**: Please refer to [Struction Perception Section](#GeneralVisualTasks).
+    - Training and evaluating for **Spatial Transformation**: Please refer to [Spatial Transformation Section](#GeneralVisualTasks).
+- **`Support Embodied Visual Reasoning Tasks`**: 
+    - Data generation and preparation: Please refer to [Embodied Visual Reasoning Tasks](#EmbodiedVisualReasoningTasks).
+    - Training and evaluating for **Embodied Planning**: Please refer to [Embodied Planning Section](#EmbodiedVisualReasoningTasks).
+    - Training and evaluating for **Embodied Affordance**: Please refer to [Embodied Affordance Section](#EmbodiedVisualReasoningTasks).
+    - Training and evaluating for **Embodied Trajectory**: Please refer to [Embodied Trajectory Section](#EmbodiedVisualReasoningTasks).
+    - Training and evaluating for **Embodied Pointing**: Please refer to [Embodied Pointing Section](#EmbodiedVisualReasoningTasks).
 
-- **`2025-04-12`**: ⭐️ We released our [Models](https://huggingface.co/tanhuajie2001/Reason-RFT-Spatial-Transformation-Qwen2-VL-2B) to huggingface for [General Visual Reasoning Tasks](#GeneralVisualTasks).
+
+## 🗞️ News
+- **`2025-09-18`**: 🔥🔥🔥 **Reason-RFT** gets accepted to NeurIPS 2025! See you in Mexico City and San Diego, USA!
+- **`2025-06-06`**: 🤖 We're excited to announce the release of our more powerful [RoboBrain 2.0](https://github.com/FlagOpen/RoboBrain2.0) using Reason-RFT.
+- **`2025-04-13`**: ✨ We released our [model zoo](https://github.com/tanhuajie/Reason-RFT?tab=readme-ov-file#--model-zoo) to huggingface.
 - **`2025-04-04`**: 🤗 We released our [datasets](https://huggingface.co/datasets/tanhuajie2001/Reason-RFT-CoT-Dataset/) to huggingface for [General Visual Reasoning Tasks](#GeneralVisualTasks).
 - **`2025-04-02`**: 🔥 We released codes and scripts for training/evaluation on [General Visual Reasoning Tasks](#GeneralVisualTasks).
 - **`2025-03-29`**: 🌍 We released the [repository](https://github.com/tanhuajie/Reason-RFT/) and [roadmap](#RoadMap) for **Reason-RFT**.
 - **`2025-03-26`**: 📑 We released our initial [ArXiv paper](https://arxiv.org/abs/2503.20752/) of **Reason-RFT**.
 
 
-## ⭐️ Usage
+## <a id="Method">⭐️ Pipeline</a>
+
+<div align="center">
+<img src="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/pipeline.png" />
+</div>
+
+
+## <a id="ModelCheckpoints"> 🤗 Model Zoo</a>
+
+| Tasks                  | Reason-RFT-Zero-2B        | Reason-RFT-Zero-7B       | Reason-RFT-2B        | Reason-RFT-7B             |
+|------------------------|---------------------------|---------------------|---------------------------|---------------------------|
+| Visual Counting        | [🤗VC-GRPO-Zero-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Visual-Counting-Qwen2-VL-2B) | [🤗VC-GRPO-Zero-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Visual-Counting-Qwen2-VL-7B) | [🤗VC-GRPO-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Visual-Counting-Qwen2-VL-2B) | [🤗VC-GRPO-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Visual-Counting-Qwen2-VL-7B) |
+| Structure Perception   | [🤗SP-GRPO-Zero-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Structure-Perception-Qwen2-VL-2B) | [🤗SP-GRPO-Zero-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Structure-Perception-Qwen2-VL-7B) | [🤗SP-GRPO-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Structure-Perception-Qwen2-VL-2B) | [🤗SP-GRPO-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Structure-Perception-Qwen2-VL-7B) |
+| Spatial Transformation | [🤗ST-GRPO-Zero-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Spatial-Transformation-Qwen2-VL-2B) | [🤗ST-GRPO-Zero-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Spatial-Transformation-Qwen2-VL-7B) | [🤗ST-GRPO-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Spatial-Transformation-Qwen2-VL-2B) | [🤗ST-GRPO-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Spatial-Transformation-Qwen2-VL-7B) | 
+
+
+## <a id="GeneralVisualTasks"> 🎲 General Visual Reasoning Tasks</a>
+
+### 🛠️ Setup
+
+```bash
+# clone repo.
+git clone https://github.com/tanhuajie/Reason-RFT.git
+cd Reason-RFT
+
+# build conda env. for stage_rl
+conda create -n reasonrft_rl python=3.10
+conda activate reasonrft_rl
+pip install -r requirements_rl.txt
+
+# build conda env. for stage_sft
+conda create -n reasonrft_sft python=3.10
+conda activate reasonrft_sft
+pip install -r requirements_sft.txt
+```
+
+### <a id="Preparation"> ♣️ Dataset Preparation</a>
+
+#### Step 1: Download Dataset
+```bash
+# Download from tanhuajie2001/Reason-RFT-CoT-Dataset
+huggingface-cli download --repo-type dataset --resume-download tanhuajie2001/Reason-RFT-CoT-Dataset --local-dir ./Reason-RFT-CoT-Dataset
+
+# unzip images
+cd Reason-RFT-CoT-Dataset
+unzip train_images.zip
+unzip test_images.zip
+```
+
+Then, your local directory should be like:
+
+```bash
+Reason-RFT-CoT-Dataset/
+│   # Images for training & evaluation
+├── images/
+│   ├── train_images/
+│   │   ├── ...
+│   └── test_images/
+│       ├── ...
+│   # CoT datasets for training
+├── train_jsons/
+��   │   # Full datasets for Spatial-Transformation task
+│   ├── A1-Spatial-Transformation-train-60k-cot.json
+│   │   # Full datasets for Structure-Perception task
+│   ├── A2-Structure-Perception-train-4k5-cot.json
+│   │   # Full datasets for Visual-Counting task
+│   ├── A3-Visual-Counting-train-35k-cot.json
+│   │   # Scientific visual reasoning (Optional for extensive/ablation exp.)
+│   ├── AI2D-train-1467-cot.json
+│   ├── ScienceQA-train-2112-cot.json
+│   │   # Topological visual reasoning (Optional for extensive/ablation exp.)
+│   ├── GVLQA-connectivity-train-1199-cot.json
+│   ├── GVLQA-cycle-train-1194-cot.json
+│   ├── GVLQA-hamilton-train-1158-cot.json
+│   ├── GVLQA-topology-train-1070-cot.json
+│   ├── GVLQA-matching-train-1193-cot.json
+│   │   # Pattern & Puzzle visual reasoning (Optional for extensive/ablation exp.)
+│   ├── PuzzleVQA-train-1618-cot.json
+│   ├── IconQA-train-5270-cot.json
+│   ├── Raven-train-982-cot.json
+│   │   # Geometric visual reasoning (Optional for extensive/ablation exp.)
+│   ├── GeoQA-train-1500-cot.json
+│   ├── GeomVerse-train-2841-cot.json
+│   └── Geometry3K-train-3794-cot.json
+│   # Datasets for evaluation
+├── test_jsons/
+│   │   # Evaluation for Spatial-Transformation task
+│   ├── Spatial-Transformation-id-test-1k.json # In-Domain
+│   ├── Spatial-Transformation-ood-left-test-1k.json # Out-of-Domain
+│   ├── Spatial-Transformation-ood-right-test-1k.json # Out-of-Domain
+│   │   # Evaluation for Structure-Perception task
+│   ├── Structure-Perception-id-test-820.json # In-Domain
+│   ├── Structure-Perception-ood-test-800.json # Out-of-Domain
+│   │   # Evaluation for Visual-Counting task
+│   ├── Visual-Counting-id-test-1k.json # In-Domain
+│   └── Visual-Counting-ood-test-1k.json # Out-of-Domain
+└── README.md
+```
+
+#### Step 2: Construct Dataset for ANS-SFT, COT-SFT, Reason-RFT(-Zero)
+
+*Note:* If you want to implement training process for our three main tasks, only three Meta-Json files -- **'A1-Spatial-Transformation-train-60k-cot.json', 'A2-Structure-Perception-train-4k5-cot.json' and 'A3-Visual-Counting-train-35k-cot.json'** -- are needed respectively. Then, all the training-used json files can be constructed by these three Meta-Json files above. At this step, you should write the simple scripts to complete the construction for **your own training-used json files** directly, according to the sample format as shown below.
+
+**📑 1. For *ANS-SFT* training, we use ShareGPT format to refactor each sample:**
+
+```json
+{
+    "id": "{id}",
+    "image": "{image}",
+    "messages": [
+        {
+            "content": "{PROMPT_xxx_ANS_SFT} + <image> + {problem}",
+            "role": "user"
+        },
+        {
+            "content": "{answer}",
+            "role": "assistant"
+        }
+    ]
+},
+```
+```
+Tips: {Prompt_xxx_ANS_SFT} can be found in ./utils/prompts.py, while {id}, {image}, {problem} and {answer} are from Meta-Json files.
+```
+
+**📑 2. For *COT-SFT* training, we also use ShareGPT format to refactor each sample:**
+
+```json
+{
+    "id": "{id}",
+    "image": "{image}",
+    "messages": [
+        {
+            "content": "{PROMPT_xxx_COT_SFT} + <image> + {problem}",
+            "role": "user"
+        },
+        {
+            "content": "<think>
+{cot}
+</think>
+<answer>
+{answer}
+</answer>",
+            "role": "assistant"
+        }
+    ]
+},
+```
+```
+Tips: {Prompt_xxx_COT_SFT} can be found in ./utils/prompts.py, while {id}, {image}, {problem}, {cot} and {answer} are from Meta-Json files.
+
+Note: when refactor 'Structure Perception' task, {Prompt_xxx_COT_SFT} can be devided into 'PROMPT_STRUCTURE_PERCEPTION_CHOICE_COT_SFT' and 'PROMPT_STRUCTURE_PERCEPTION_NON_CHOICE_COT_SFT'. If 'A' or 'B' or 'C' or 'D' in {answer}, we use 'PROMPT_STRUCTURE_PERCEPTION_CHOICE_COT_SFT', otherwise, we use 'PROMPT_STRUCTURE_PERCEPTION_NON_CHOICE_COT_SFT'.
+```
+
+**📑 3. For *Reason-RFT-Zero* training, we use RL format below to refactor each sample:**
+
+```json
+{
+    "id": "{id}",
+    "image": "{image}",
+    "problem": "{problem}",
+    "solution": "{answer}"
+},
+```
+```
+Tips: {id}, {image}, {problem} and {answer} are from Meta-Json files.
+```
+
+**📑 4. For *Reason-RFT* training, we use COT-SFT format to refactor 1.6k samples for STAGE-1, and use RL format to refactor the rest samples or full samples for STAGE-2.**
 
-*Please refer to [Reason-RFT](https://github.com/tanhuajie/Reason-RFT) for more details.*
+ *Specifically, in STAGE 2, we refactor full samples for Structure-Perception task due to its too limited training samples (only 4.5k), while we refactor only the rest samples for Spatial-Transformation task and Visual-Counting task.*
+
+
+#### Step 3: Change Path to Your Own Constructed Datasets
+```bash
+# SFT Training: 
+change dataset paths defined in './train/stage_sft/dataset_info.json' file.
+
+# RL Training: 
+change dataset paths defined in './scripts/train/reason_rft/stage_rl/xxx.bash' file.
+change dataset paths defined in './scripts/train/reason_rft_zero/xxx.bash' file.
+
+# Evaluation: 
+change dataset paths defined in './eval/eval_by_vllm_for_open_source.py' file.
+```
+
+### <a id="Training"> 📚 Training</a>
+
+```bash
+# ANS-SFT, Task1 (Visual-Counting), Qwen2-vl-2b
+bash scripts/train/ans_sft/resume_finetune_qwen2vl_2b_task1_ans_sft.sh
+
+# ANS-SFT, Task1 (Visual-Counting), Qwen2-vl-7b
+bash scripts/train/ans_sft/resume_finetune_qwen2vl_7b_task1_ans_sft.sh
+
+# ANS-SFT, Task2 (Structure-Perception), Qwen2-vl-2b
+bash scripts/train/ans_sft/resume_finetune_qwen2vl_2b_task2_ans_sft.sh
+
+# ANS-SFT, Task2 (Structure-Perception), Qwen2-vl-7b
+bash scripts/train/ans_sft/resume_finetune_qwen2vl_7b_task2_ans_sft.sh
+
+# ANS-SFT, Task3 (Spatial-Transformation), Qwen2-vl-2b
+bash scripts/train/ans_sft/resume_finetune_qwen2vl_2b_task3_ans_sft.sh
+
+# ANS-SFT, Task3 (Spatial-Transformation), Qwen2-vl-7b
+bash scripts/train/ans_sft/resume_finetune_qwen2vl_7b_task3_ans_sft.sh
+```
+
+```bash
+# COT-SFT, Task1 (Visual-Counting), Qwen2-vl-2b
+bash scripts/train/cot_sft/resume_finetune_qwen2vl_2b_task1_cot_sft.sh
+
+# COT-SFT, Task1 (Visual-Counting), Qwen2-vl-7b
+bash scripts/train/cot_sft/resume_finetune_qwen2vl_7b_task1_cot_sft.sh
+
+# COT-SFT, Task2 (Structure-Perception), Qwen2-vl-2b
+bash scripts/train/cot_sft/resume_finetune_qwen2vl_2b_task2_cot_sft.sh
+
+# COT-SFT, Task2 (Structure-Perception), Qwen2-vl-7b
+bash scripts/train/cot_sft/resume_finetune_qwen2vl_7b_task2_cot_sft.sh
+
+# COT-SFT, Task3 (Spatial-Transformation), Qwen2-vl-2b
+bash scripts/train/cot_sft/resume_finetune_qwen2vl_2b_task3_cot_sft.sh
+
+# COT-SFT, Task3 (Spatial-Transformation), Qwen2-vl-7b
+bash scripts/train/cot_sft/resume_finetune_qwen2vl_7b_task3_cot_sft.sh
+```
+
+```bash
+# Reason-RFT-Zero, Task1 (Visual-Counting), Qwen2-vl-2b
+bash scripts/train/reason_rft_zero/resume_finetune_qwen2vl_2b_task1_only_rl.sh
+
+# Reason-RFT-Zero, Task1 (Visual-Counting), Qwen2-vl-7b
+bash scripts/train/reason_rft_zero/resume_finetune_qwen2vl_7b_task1_only_rl.sh
+
+# Reason-RFT-Zero, Task2 (Structure-Perception), Qwen2-vl-2b
+bash scripts/train/reason_rft_zero/resume_finetune_qwen2vl_2b_task2_only_rl.sh
+
+# Reason-RFT-Zero, Task2 (Structure-Perception), Qwen2-vl-7b
+bash scripts/train/reason_rft_zero/resume_finetune_qwen2vl_7b_task2_only_rl.sh
+
+# Reason-RFT-Zero, Task3 (Spatial-Transformation), Qwen2-vl-2b
+bash scripts/train/reason_rft_zero/resume_finetune_qwen2vl_2b_task3_only_rl.sh
+
+# Reason-RFT-Zero, Task3 (Spatial-Transformation), Qwen2-vl-7b
+bash scripts/train/reason_rft_zero/resume_finetune_qwen2vl_7b_task3_only_rl.sh
+```
+
+```bash
+# Reason-RFT, Task1 (Visual-Counting), Qwen2-vl-2b, STAGE1 + STAGE2
+bash scripts/train/reason_rft/stage_sft/resume_finetune_qwen2vl_2b_task1_stage1_sft.sh
+bash scripts/train/reason_rft/stage_rl/resume_finetune_qwen2vl_2b_task1_stage2_rl.sh
+
+# Reason-RFT, Task1 (Visual-Counting), Qwen2-vl-7b, STAGE1 + STAGE2
+bash scripts/train/reason_rft/stage_sft/resume_finetune_qwen2vl_7b_task1_stage1_sft.sh
+bash scripts/train/reason_rft/stage_rl/resume_finetune_qwen2vl_7b_task1_stage2_rl.sh
+
+# Reason-RFT, Task2 (Structure-Perception), Qwen2-vl-2b, STAGE1 + STAGE2
+bash scripts/train/reason_rft/stage_sft/resume_finetune_qwen2vl_2b_task2_stage1_sft.sh
+bash scripts/train/reason_rft/stage_rl/resume_finetune_qwen2vl_2b_task2_stage2_rl.sh
+
+# Reason-RFT, Task2 (Structure-Perception), Qwen2-vl-7b, STAGE1 + STAGE2
+bash scripts/train/reason_rft/stage_sft/resume_finetune_qwen2vl_7b_task2_stage1_sft.sh
+bash scripts/train/reason_rft/stage_rl/resume_finetune_qwen2vl_7b_task2_stage2_rl.sh
+
+# Reason-RFT, Task3 (Spatial-Transformation), Qwen2-vl-2b, STAGE1 + STAGE2
+bash scripts/train/reason_rft/stage_sft/resume_finetune_qwen2vl_2b_task3_stage1_sft.sh
+bash scripts/train/reason_rft/stage_rl/resume_finetune_qwen2vl_2b_task3_stage2_rl.sh
+
+# Reason-RFT, Task3 (Spatial-Transformation), Qwen2-vl-7b, STAGE1 + STAGE2
+bash scripts/train/reason_rft/stage_sft/resume_finetune_qwen2vl_7b_task3_stage1_sft.sh
+bash scripts/train/reason_rft/stage_rl/resume_finetune_qwen2vl_7b_task3_stage2_rl.sh
+```
+**Note:** Please change the dataset, pre-trained model and image path in the scripts above.
+
+## <a id="Evaluation"> 🔭 Evaluation</a>
+```bash
+# Evaluating and get scores for Proprietary Models (GPT4o, Gemini)
+bash scripts/eval/close_source_models/evaluate_gemini.sh
+bash scripts/eval/close_source_models/evaluate_gpt4o.sh
+
+# Only get scores for Proprietary Models (GPT4o, Gemini), since you have already evaluated them before
+bash scripts/eval/close_source_models/evaluate_gemini_only_calculate_score.sh
+bash scripts/eval/close_source_models/evaluate_gpt4o_only_calculate_score.sh
+
+# Evaluating with single GPU for ours or Open-Source Models
+bash scripts/eval/open_source_models/single_gpu_eval/eval_by_vllm_all_tasks_zero_shot_single_gpu.sh
+bash scripts/eval/open_source_models/single_gpu_eval/eval_by_vllm_all_tasks_ans_sft_single_gpu.sh
+bash scripts/eval/open_source_models/single_gpu_eval/eval_by_vllm_all_tasks_cot_sft_single_gpu.sh
+bash scripts/eval/open_source_models/single_gpu_eval/eval_by_vllm_all_tasks_reason_rft_single_gpu.sh
+
+# Evaluating with multiple GPUs for ours or Open-Source Models
+bash scripts/eval/open_source_models/multi_gpu_eval/eval_by_vllm_all_tasks_zero_shot_multi_gpu.sh
+bash scripts/eval/open_source_models/multi_gpu_eval/eval_by_vllm_all_tasks_ans_sft_multi_gpu.sh
+bash scripts/eval/open_source_models/multi_gpu_eval/eval_by_vllm_all_tasks_cot_sft_multi_gpu.sh
+bash scripts/eval/open_source_models/multi_gpu_eval/eval_by_vllm_all_tasks_reason_rft_multi_gpu.sh
+
+# Get scores for ours or Open-Source Models
+bash scripts/eval/open_source_models/calculate_score/calculate_score.sh
+```
+**Note:** Please change the checkpoint path in the scripts above.
+
+### <a id="OurResults"> ⭐️ Results in our paper</a>
+
+*1. Results of Spatial Transformation Task:*
+
+<div align="center">
+<img src="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/res_task3.png" />
+</div>
+
+
+*2. Full Results of Three Tasks:*
+
+<div align="center">
+<img src="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/result.png" />
+</div>
+
+*Note: For more detail results, please refer to our [main paper and appendix](https://arxiv.org/abs/2503.20752)*
+
+## <a id="EmbodiedVisualReasoningTasks"> 🤖 Embodied Visual Reasoning Tasks</a>
+We apply Reason-RFT to train more powerful RoboBrain 2.0. Please refer to [RoboBrain 2.0 Github](https://github.com/FlagOpen/RoboBrain2.0) for more details. Here is a simplified comparison result (not the final version):
+
+<div align="center">
+<img src="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/rb2_res.png" />
+</div>
 
 ## 📑 Citation
 If you find this project useful, welcome to cite us.
@@ -74,4 +404,18 @@ If you find this project useful, welcome to cite us.
   journal={arXiv preprint arXiv:2503.20752},
   year={2025}
 }
+
+@article{team2025robobrain,
+  title={Robobrain 2.0 technical report},
+  author={Team, BAAI RoboBrain and Cao, Mingyu and Tan, Huajie and Ji, Yuheng and Lin, Minglan and Li, Zhiyu and Cao, Zhou and Wang, Pengwei and Zhou, Enshen and Han, Yi and others},
+  journal={arXiv preprint arXiv:2507.02029},
+  year={2025}
+}
+
+@article{ji2025robobrain,
+  title={RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete},
+  author={Ji, Yuheng and Tan, Huajie and Shi, Jiayu and Hao, Xiaoshuai and Zhang, Yuan and Zhang, Hengyuan and Wang, Pengwei and Zhao, Mengdi and Mu, Yao and An, Pengju and others},
+  journal={arXiv preprint arXiv:2502.21257},
+  year={2025}
+}
 ```