| # M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning | |
| π [Technical Report](./assets/M2-Reasoning.pdf) | π [arXiv](https://arxiv.org/abs/2507.08306) | π€ [Hugging Face](https://huggingface.co/inclusionAI/M2-Reasoning)ο½ π€ [ModelScope](https://www.modelscope.cn/models/inclusionAI/M2-Reasoning) | |
| ## Introduction | |
| We introduce M2-Reasoning-7B, a model designed to excel in both general and spatial reasoning. Our approach integrates two key innovations: (1) a novel data pipeline that generates 294.2K high-quality data samples (168K for cold-start fine-tuning and 126.2K for RLVR), which feature logically coherent reasoning trajectories and have undergone comprehensive assessment; and (2) a dynamic multi-task training strategy with step-wise optimization to mitigate conflicts between data, and task-specific rewards for delivering tailored incentive signals. This combination of curated data and advanced training allows M2-Reasoning-7B to set a new state-of-the-art (SOTA) across 8 benchmarks, showcasing superior performance in both general and spatial reasoning domains. | |
|  | |
| ## π Updates | |
| - [2025.07.14] π₯ Our Technical Report is available on π [arXiv](https://arxiv.org/abs/2507.08306). | |
| - [2025.07.11] π₯ We release M2-Reasoning on π€ [Hugging Face](https://huggingface.co/inclusionAI/M2-Reasoning) and π€ [ModelScope](https://www.modelscope.cn/models/inclusionAI/M2-Reasoning). | |
| ## Key Features | |
| - A High-quality Data Construction Pipeline: We design and implement a multi-stage data synthesis and curation pipeline that generates vast amounts of reasoning data. | |
| - A Dynamic Multi-Task Training Strategy: We propose a sophisticated training strategy that effectively handles data heterogeneity. It features step-wise dynamic optimization to mitigate conflicts between different data sources and a task-specific reward formulation to provide tailored incentive signals. | |
| - Unified General and Spatial Reasoning Model: We propose M2-Reasoning-7B, an MLLM uniquely engineered for both abstract and spatial reasoning. Extensive evaluations on 8 distinctbenchmarks demonstrate that, by leveraging our custom data and training pipelines, M2-Reasoning establishes new state-of-the-art (SOTA) results across both general and spatial reasoning domains. | |
| ## Evaluation | |
| We conduct a comprehensive evaluation of our models across two key domains: general and spatial | |
| reasoning. Our evaluation utilizes a diverse set of public benchmarks, grouped by the primary | |
| capability they measure: | |
| - General Reasoning (Mathematical & Logical): To evaluate this capability, we employ six benchmarks: MathVista, MathVision, MathVerse, DynaMath, WeMath, and LogicVista. | |
| |Models| MathVista| MathVision| MathVerse| DynaMath| WeMath| LogicVista| Avg. (Ξ)| | |
| |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | |
| |***Base-Scale General Models***| | |
| |InternVL3-8B | 70.5| 30.0| 38.5| 25.7 |39.5 |44.5 |41.4| | |
| |InternVL3-9B | 69.0 | 29.3| 37.9 |25.1 |34.8| 49.0 |40.8| | |
| |Qwen2.5-VL-7B |68.1 |25.4 |41.1 |21.8 |36.2| 47.9| 40.1| | |
| |MUG-U-7B | 74.8 |26.1 |35.4 |17.2 |26.5 |39.8| 36.6| | |
| |SAIL-VL-1.6-8B | 74.2 |23.2| 33.4 |14.0 |29.6 |41.4| 36.0| | |
| |***Base-Scale Reasoning Models***| | |
| |WeThink-VL-7B| 71.6 |26.0| 44.2 |24.8 |**48.0** |**51.2**| 44.3 (+4.2)| | |
| |Taichu-VLR-7B | 72.3| 27.1 |46.7 |23.0 |44.0 |48.3 |43.6| | |
| |VLAA-Thinker-7B | 68.0 |26.4| **48.2** |22.4 |41.5 |48.5 |42.5 (+2.4)| | |
| |URSA-8B-PS-GRPO | 67.8 |**31.8** |41.5 |22.4| 38.3 |44.7 |41.1 (+8.2)| | |
| |Ovis2-8B |71.8 |25.9| 42.3 |20.4 |27.2 |39.4| 37.8| | |
| |***Our Models***| | |
| |Base Model |70.2| 25.9| 30.5| 20.2| 27.2| 37.8| 35.5| | |
| |M2-Reasoning-CI-7B| 71.7| 29.2| 42.1| 25.0 |42.8| 46.8 |42.9 (+7.4)| | |
| |M2-Reasoning-7B | **75.0** |31.5| 44.7 |**26.8** |41.8 |50.0 |**45.0 (+9.5)**| | |
| |M2-Reasoning-7B-HF* | 74.7 |30.5| 46.1 |26.8 |42.7 |49.2 |45.0 (+9.5)| | |
| \* After converting the checkpoints to huggingface, the accuracies are slightly different. | |
| - Spatial Reasoning: We assess this skill using 2 benchmarks: CV-Bench and VSI-Bench | |
| - CV-Bench: | |
| | Models | Count | Relation | Depth | Distance | Avg. | | |
| | :--- | :---: | :---: | :---: | :---: | :---: | | |
| | ***Large-Scale Models*** | | | | | | | |
| | GPT-4O | 65.9 | 85.7 | 87.8 | 78.2 | 78.9 | | |
| | Gemini-1.5-pro | 70.4 | 85.2 | 82.4 | 72.8 | 77.4 | | |
| | ***Base-Scale Models*** | | | | | | | |
| | InternVL3-8B| **74.0** | 90.6 | 84.3 | 81.0 | 82.0 | | |
| | Qwen2.5-VL-7B-Instruct | 65.2 | 86.6 | 70.6 | 79.8 | 75.0 | | |
| | LLava-NEXT-Video-7B | 59.3 | 77.0 | 71.3 | 54.7 | 65.2 | | |
| | ***Our Models*** | | | | | | | |
| | M2-Reasoning-7B | 66.6 | **92.8** | **89.3** | **84.3** | **82.3** | | |
| - VSI-Bench: | |
| | | OC | AD| OS|RS |RDs |RDr |RP |AO |Avg. | | |
| | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | |
| | ***Large-Scale Models*** | | | | | | | | | | | |
| | Gemini-1.5-pro | 56.2 | 30.9 | 64.1 | 43.6 | 51.3 | 46.3 | 36.0 | 34.6 | 45.4 | | |
| | GPT-4O | 46.2 | 5.3 | 43.8 | 38.2 | 37.0 | 41.3 | 31.5 | 28.5 | 34.0 | | |
| | ***Base-Scale Models*** | | | | | | | | | | | |
| | InternVL3-8B | **68.1** | **39.0** | 48.4 | 33.6 | **48.3** | 36.4 | 27.3 | **35.4** | 42.1 | | |
| | Video-R1-7B | - | - | - | - | - | - | - | - | 37.1 | | |
| | Qwen2.5-VL-7B-Instruct| 37.7 | 20.1 | 49.7 | 37.4 | 38.5 | 40.4 | 31.4 | 32.0 | 35.9 | | |
| | LLava-NeXT-Video-7B| 48.5 | 14.0 | 47.8 | 24.2 | 43.5 | 42.4 | **34.0** | 30.6 | 35.6 | | |
| | ***Our Models*** | | | | | | | | | | | |
| | M2-Reasoning-7B | 41.0 | 34.0 | **60.9** | **55.4** | 40.7 | **47.3** | 29.9 | 28.8 | **42.3** | | |
| ## Model Downloads | |
| You can download the model from both π€ [Hugging Face](https://huggingface.co/inclusionAI/M2-Reasoning) and π€ [ModelScope](https://www.modelscope.cn/models/inclusionAI/M2-Reasoning). | |
| ## Installation | |
| Please download our model following Model Downloads, then you can refer to the following codes to run M2-Reasoning model. | |
| The basic environment is `python=3.10`, `torch=2.6.0+cu124`, `transformers=4.49.0` | |
| ## Example Usage | |
| We provide a small example on the usage of this repo. For detailed usage. | |
| ``` python | |
| import os | |
| import torch | |
| from transformers import ( | |
| AutoProcessor, | |
| AutoTokenizer, | |
| ) | |
| import warnings | |
| import argparse | |
| from modeling_bailing_qwen2_5 import Bailing_qwen2_5NativeForConditionalGeneration | |
| from processing_bailing_qwen2_5 import Bailing_qwen2_5Processor | |
| warnings.filterwarnings("ignore") | |
| class BailingMMInfer: | |
| def __init__(self, | |
| model_name_or_path, | |
| device="cuda", | |
| max_pixels=None, | |
| min_pixels=None, | |
| video_max_pixels=768 * 28 * 28, | |
| video_min_pixels=128 * 28 * 28, | |
| generation_config=None | |
| ): | |
| super().__init__() | |
| self.model_name_or_path = model_name_or_path | |
| self.device = device | |
| self.device_map = device | |
| self.video_max_pixels = video_max_pixels if video_max_pixels is not None else 768 * 28 * 28 | |
| self.video_min_pixels = video_min_pixels if video_min_pixels is not None else 128 * 28 * 28 | |
| self.model, self.tokenizer, self.processor = self.load_model_processor() | |
| if max_pixels is not None: | |
| self.processor.max_pixels = max_pixels | |
| if min_pixels is not None: | |
| self.processor.min_pixels = min_pixels | |
| if generation_config is None: | |
| generation_config = { | |
| "num_beams": 1, | |
| "do_sample": True, | |
| "temperature": 0.9 | |
| } | |
| self.generation_config = generation_config | |
| def load_model_processor(self): | |
| model = Bailing_qwen2_5NativeForConditionalGeneration.from_pretrained( | |
| self.model_name_or_path, | |
| torch_dtype=torch.bfloat16, | |
| device_map=self.device_map, | |
| _attn_implementation="flash_attention_2" | |
| ).eval() | |
| tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, add_bos_token=True, trust_remote_code=True) | |
| processor = Bailing_qwen2_5Processor.from_pretrained(self.model_name_or_path, trust_remote_code=True) | |
| return model, tokenizer, processor | |
| def generate(self, messages, max_new_tokens=512): | |
| text = self.processor.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True, use_system=True | |
| ) | |
| image_inputs, video_inputs = self.processor.process_vision_info(messages) | |
| inputs = self.processor( | |
| text=[text], | |
| images=image_inputs, | |
| videos=video_inputs, | |
| return_tensors="pt", | |
| ) | |
| # print(inputs) | |
| print(self.tokenizer.decode(inputs['input_ids'][0])) | |
| inputs = inputs.to(self.device) | |
| for k in inputs.keys(): | |
| if k == "pixel_values" or k == "pixel_values_videos": | |
| inputs[k] = inputs[k].to(dtype=torch.bfloat16) | |
| with torch.no_grad(): | |
| generated_ids = self.model.generate( | |
| inputs, | |
| max_new_tokens=max_new_tokens, | |
| eos_token_id=self.processor.tokenizer.eos_token_id, | |
| **self.generation_config, | |
| ) | |
| generated_ids_trimmed = [ | |
| out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) | |
| ] | |
| output_text = self.processor.batch_decode( | |
| generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False | |
| )[0] | |
| return output_text | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--model_name_or_path', type=str, default="inclusionAI/M2-Reasoning") | |
| parser.add_argument('--max_pixels', type=int, default=401408) | |
| parser.add_argument('--min_pixels', type=int, default=401408) | |
| parser.add_argument('--max_new_tokens', type=int, default=4096) | |
| args = parser.parse_args() | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # model_name_or_path = os.path.join(args.input_dir, args.model_name_or_path) | |
| bailing2 = BailingMMInfer( | |
| args.model_name_or_path, | |
| device=device, | |
| max_pixels=args.max_pixels, | |
| min_pixels=args.min_pixels | |
| ) | |
| messages = [ | |
| { | |
| "role": "system", | |
| "content": [ | |
| {"type": "text", "text": "You are a helpful assistant. When the user asks a question, your response must include two parts: first, the reasoning process enclosed in <think>...</think> tags, then the final answer enclosed in <answer>...</answer> tags. The critical answer or key result should be placed within \\boxed{}."}]}, | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "image", "image": "./assets/example1.png"}, | |
| {"type": "text", "text": "\nQuestion:\n\nRhombus $QRST$ has an area of 137.9 square meters. If $RT$ is 12.2 meters, find $QS$.\nA. 11.3\nB. 22.4\nC. 22.6\nD. 25.6"}, | |
| ], | |
| }, | |
| ] | |
| output_text = bailing2.generate(messages, max_new_tokens=args.max_new_tokens) | |
| print(output_text) | |
| ''' | |
| [Output]: | |
| <think> | |
| To find the length of \( QS \) in the rhombus \( QRST \), we can use the formula for the area of a rhombus, which is given by: | |
| \[ | |
| \text{Area} = \frac{1}{2} \times d_1 \times d_2 | |
| \] | |
| where \( d_1 \) and \( d_2 \) are the lengths of the diagonals. In this problem, we are given: | |
| - The area of the rhombus is 137.9 square meters. | |
| - One of the diagonals, \( RT \), is 12.2 meters. | |
| We need to find the length of the other diagonal, \( QS \). | |
| Let's denote: | |
| - \( d_1 = RT = 12.2 \) meters | |
| - \( d_2 = QS \) | |
| Substitute the known values into the area formula: | |
| \[ | |
| 137.9 = \frac{1}{2} \times 12.2 \times QS | |
| \] | |
| To solve for \( QS \), first multiply both sides by 2 to eliminate the fraction: | |
| \[ | |
| 275.8 = 12.2 \times QS | |
| \] | |
| Next, divide both sides by 12.2: | |
| \[ | |
| QS = \frac{275.8}{12.2} | |
| \] | |
| Now, perform the division: | |
| \[ | |
| QS \approx 22.6 | |
| \] | |
| So, the length of \( QS \) is approximately 22.6 meters. | |
| Looking at the options provided: | |
| A. 11.3 | |
| B. 22.4 | |
| C. 22.6 | |
| D. 25.6 | |
| The correct answer is C. 22.6. | |
| </think> | |
| <answer> | |
| \boxed{C. 22.6} | |
| </answer><|im_end|> | |
| ''' | |
| ``` | |
| ## License and Legal Disclaimer | |
| This code repository is licensed under the MIT License, and the Legal Disclaimer is located in the LEGAL.md file under the project's root directory. | |
| ## Citation | |
| If you find our work helpful, feel free to give us a cite. | |
| ``` | |
| @misc{M2reasoning2025, | |
| title = {M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning}, | |
| author = {Inclusion AI}, | |
| year = {2025}, | |
| archivePrefix = {arXiv}, | |
| } | |
| ``` | |