# M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning 📖 [Technical Report](./assets/M2-Reasoning.pdf) | 📄 [arXiv](https://arxiv.org/abs/2507.08306) | 🤗 [Hugging Face](https://huggingface.co/inclusionAI/M2-Reasoning)｜ 🤖 [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. ![](assets/teaser.png) ## 📌 Updates - [2025.07.14] 🔥 Our Technical Report is available on 📄 [arXiv](https://arxiv.org/abs/2507.08306). - [2025.07.11] 🔥 We release M2-Reasoning on 🤗 [Hugging Face](https://huggingface.co/inclusionAI/M2-Reasoning) and 🤖 [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. (Δ)| |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |***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 🤗 [Hugging Face](https://huggingface.co/inclusionAI/M2-Reasoning) and 🤖 [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 ... tags, then the final answer enclosed in ... 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]: 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. \boxed{C. 22.6} <|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}, } ```