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Model: tanhuajie2001/Robo-Dopamine-GRM-2.0-4B-Preview
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen3-VL-4B-Instruct
+pipeline_tag: image-text-to-text
+---
+<h1 align="center">Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation</h1>
+
+<h3 align="center">Joy is dopamine’s handiwork—whether in humans or in robotics.</h3>
+
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2512.23703"><img src="https://img.shields.io/badge/arXiv-2512.23703-b31b1b.svg" alt="arXiv"></a>
+  &nbsp;
+  <a href="https://robo-dopamine.github.io/"><img src="https://img.shields.io/badge/%F0%9F%8F%A0%20Project-Homepage-blue" alt="Project Homepage"></a>
+  &nbsp;
+  <a href="https://github.com/FlagOpen/Robo-Dopamine"><img src="https://img.shields.io/badge/🔍%20Code-Github-orange" alt="Github"></a>
+  &nbsp;
+  <a href="https://huggingface.co/collections/tanhuajie2001/robo-dopamine"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Weights-Huggingface-yellow" alt="Weights"></a>
+  &nbsp;
+  <a href="#"><img src="https://img.shields.io/badge/🤗%20Dataset-Stay%20tuned-green.svg" alt="Dataset"></a>
+
+</p>
+
+
+<div style="text-align: center; background-color: white;">
+    <img src="https://github.com/FlagOpen/Robo-Dopamine/raw/main/assets/teasor.png" width=100% >
+</div>
+
+
+## 🗞️ News
+- **`2026-04-05`**: 🤗 We released [Robo-Dopamine-GRM-2.0-4B-Preview](https://huggingface.co/tanhuajie2001/Robo-Dopamine-GRM-2.0-4B-Preview) model.
+- **`2026-03-05`**: 🤗 We released [Robo-Dopamine-GRM-2.0-8B-Preview](https://huggingface.co/tanhuajie2001/Robo-Dopamine-GRM-2.0-8B-Preview) model. More General, More Powerful!!!
+- **`2026-03-02`**: 🤗 We released [Robo-Dopamine-GRM-8B](https://huggingface.co/tanhuajie2001/Robo-Dopamine-GRM-8B) model
+- **`2026-02-22`**: 🔥🔥🔥 **Robo-Dopamine** gets accepted to CVPR 2026! See you in Denver, Colorado, USA!
+- **`2026-02-10`**: ⚡  We released data generation pipeline and finetune codes. ***Try to finetune with your own data***.
+- **`2026-01-26`**: 🔍 We released [Robo-Dopamine-Bench](https://huggingface.co/datasets/tanhuajie2001/Robo-Dopamine-Bench) benchmark and evaluation codes.
+- **`2026-01-08`**: 🤗 We released [Robo-Dopamine-GRM-3B](https://huggingface.co/tanhuajie2001/Robo-Dopamine-GRM-3B) model and inference codes.
+- **`2025-12-30`**: ✨ ***Codes, Dataset and Weights are coming soon! Stay tuned for updates***.
+- **`2025-12-30`**: 🔥 We released our [Project Page](https://robo-dopamine.github.io/) of **Robo-Dopamine**.
+
+
+## 🤖 Overview
+
+**Robo-Dopamine** is composed of two core components: ***(a) Dopamine-Reward Modeling Method --*** At the heart of our reward modeling is to build the General Reward Model (GRM), a vision-language model that is prompted with a task description and conditioned on multi-view images of initial, goal, "BEFORE," and "AFTER" states to predict a relative progress or regress hop. To ensure a stable and accurate signal, we employ *Multi-Perspective Progress Fusion*, which combines incremental, forward-anchored, and backward-anchored predictions into a final fused reward. And ***(b) Dopamine-RL Training Framework --*** The Dopamine-RL framework first adapts the pre-trained GRM to a novel task using a single demonstration, i.e., *One-Shot GRM Adaptation*. Subsequently, it uses a theoretically-sound *Policy-Invariant Reward Shaping* method to convert the GRM's dense output into a reward signal that accelerates learning without altering the optimal policy. 
+This approach is universally compatible with a wide range of RL algorithms.
+
+<div align="center"> 
+    <img src="https://github.com/FlagOpen/Robo-Dopamine/raw/main/assets/method.png" alt="Logo" style="width=100%;vertical-align:middle">
+</div>
+
+## 🛠️ Setup
+
+```bash
+# clone repo.
+git clone https://github.com/FlagOpen/Robo-Dopamine.git
+cd Robo-Dopamine
+
+# build conda env.
+conda create -n robo-dopamine python=3.10
+conda activate robo-dopamine
+pip install -r requirements.txt
+```
+
+## 💡 Simple Inference
+
+```python
+import os
+from examples.inference import GRMInference
+
+model = GRMInference("tanhuajie2001/Robo-Dopamine-GRM-2.0-4B-Preview")
+
+TASK_INSTRUCTION = "organize the table"
+BASE_DEMO_PATH = "./examples/demo_table"
+OUTPUT_ROOT = "./results"
+
+## Note: If no target/goal image is provided, 
+## please replace `GOAL_IMAGE_PATH` with the blank image "./examples/demo_table/blank_goal.png".
+GOAL_IMAGE_PATH = "./examples/demo_table/goal_image.png" # "./examples/demo_table/blank_goal.png"
+
+# select prediction model: Forward-Mode, Incremental-Mode or Backward-Mode
+PREDICTION_MODE = "forward" # "incremental" or "backward"
+
+# multi-view usage:
+output_dir = model.run_pipeline(
+    cam_high_path  = os.path.join(BASE_DEMO_PATH, "cam_high.mp4"),
+    cam_left_path  = os.path.join(BASE_DEMO_PATH, "cam_left_wrist.mp4"),
+    cam_right_path = os.path.join(BASE_DEMO_PATH, "cam_right_wrist.mp4"),
+    out_root       = OUTPUT_ROOT,
+    task           = TASK_INSTRUCTION,
+    frame_interval = 10, # modify frame_interval as desired, but it shouldn't be set too small if using 'incremental'.
+    batch_size     = 1, # please increase batch_size > 1, if you have enough GPU memory.
+    goal_image     = GOAL_IMAGE_PATH,
+    eval_mode      = PREDICTION_MODE,
+    visualize      = True
+)
+print(f"Episode ({BASE_DEMO_PATH}) processed with multi-view {PREDICTION_MODE}-mode. Output at: {output_dir}")
+
+# single-view usage:
+output_dir = model.run_pipeline(
+    cam_high_path  = os.path.join(BASE_DEMO_PATH, "cam_high.mp4"),
+    cam_left_path  = os.path.join(BASE_DEMO_PATH, "cam_high.mp4"), # repeat cam_high
+    cam_right_path = os.path.join(BASE_DEMO_PATH, "cam_high.mp4"), # repeat cam_high
+    out_root       = OUTPUT_ROOT,
+    task           = TASK_INSTRUCTION,
+    frame_interval = 10, # modify frame_interval as desired, but it shouldn't be set too small if using 'incremental'.
+    batch_size     = 1, # please increase batch_size > 1, if you have enough GPU memory.
+    goal_image     = GOAL_IMAGE_PATH,
+    eval_mode      = PREDICTION_MODE,
+    visualize      = True
+)
+print(f"Episode ({BASE_DEMO_PATH}) processed with single-view {PREDICTION_MODE}-mode. Output at: {output_dir}")
+
+```
+
+## 📑 Citation
+
+If you find our work helpful, feel free to cite it:
+```
+@article{tan2025robo,
+  title={Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation},
+  author={Tan, Huajie and Chen, Sixiang and Xu, Yijie and Wang, Zixiao and Ji, Yuheng and Chi, Cheng and Lyu, Yaoxu and Zhao, Zhongxia and Chen, Xiansheng and Co, Peterson and others},
+  journal={arXiv preprint arXiv:2512.23703},
+  year={2025}
+}
+```

config.json

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+{
+  "architectures": [
+    "Qwen3VLForConditionalGeneration"
+  ],
+  "image_token_id": 151655,
+  "model_type": "qwen3_vl",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 9728,
+    "max_position_embeddings": 262144,
+    "model_type": "qwen3_vl_text",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        24,
+        20,
+        20
+      ],
+      "rope_type": "default"
+    },
+    "rope_theta": 5000000,
+    "tie_word_embeddings": true,
+    "use_cache": true,
+    "vocab_size": 151936
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.0.dev0",
+  "video_token_id": 151656,
+  "vision_config": {
+    "deepstack_visual_indexes": [
+      5,
+      11,
+      17
+    ],
+    "depth": 24,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1024,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "model_type": "qwen3_vl",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 2560,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652
+}

generation_config.json

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+{
+    "bos_token_id": 151643,
+    "pad_token_id": 151643,
+    "do_sample": true,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "top_k": 20,
+    "top_p": 0.8,
+    "repetition_penalty": 1.0,
+    "temperature": 0.7,
+    "transformers_version": "4.56.0"
+}

model.safetensors

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+oid sha256:e2f6e3c372f4ef76afb5e9e28ec56679944fcc3b4f2ce2ebda8b21264e96eb2f
+size 9653631848

preprocessor_config.json

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+{
+    "size": {
+        "longest_edge": 16777216,
+        "shortest_edge": 65536
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "image_processor_type": "Qwen2VLImageProcessorFast"
+}

video_preprocessor_config.json

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+{
+    "size": {
+        "longest_edge": 25165824,
+        "shortest_edge": 4096
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "video_processor_type": "Qwen3VLVideoProcessor"
+}