---
base_model: QizhiPei/Qwen2.5-Math-7B-Instruct-RoPE-300k
library_name: transformers
license: apache-2.0
tags:
- llama-factory
- full
- generated_from_trainer
pipeline_tag: text-generation
model-index:
- name: DiffScale-7B
  results: []
---

Paper: [ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning](https://arxiv.org/abs/2509.21070)

Code: https://github.com/QizhiPei/ScaleDiff

# DiffScale-7B

This model is a fine-tuned version of [QizhiPei/Qwen2.5-Math-7B-Instruct-RoPE-300k](https://huggingface.co/QizhiPei/Qwen2.5-Math-7B-Instruct-RoPE-300k) on the ScaleDiff-Math dataset.

## Model description

ScaleDiff-7B is a Large Reasoning Model (LRM) developed as part of the ScaleDiff pipeline, which is designed to scale the creation of challenging mathematical problems. This model, fine-tuned on the novel ScaleDiff-Math dataset, aims to enhance advanced mathematical reasoning capabilities by addressing the scarcity of high-quality, difficult training data. It leverages an adaptive thinking model for problem identification and a specialized generator (DiffGen-8B) for large-scale problem synthesis.

## Intended uses & limitations

ScaleDiff-7B is intended for advanced mathematical reasoning tasks, offering significant improvements in complex problem-solving. It is particularly useful for researchers and practitioners looking to benchmark and develop LRMs on difficult mathematical challenges.

**Limitations**: As a language model, its performance is dependent on the quality and scope of its training data. While designed for difficult problems, it may exhibit limitations in highly novel or out-of-distribution mathematical contexts. Further research is needed to fully understand its generalization capabilities beyond the specific benchmarks used in its evaluation.

## Training and evaluation data

ScaleDiff-7B was fine-tuned on the custom-created [ScaleDiff-Math dataset](https://huggingface.co/datasets/QizhiPei/ScaleDiff-Math). This dataset is generated through a three-step pipeline:
1.  **Problem Selection**: Difficult problems are identified from the [AM-Distilled-Dataset](https://huggingface.co/datasets/a-m-team/AM-Qwen3-Distilled) using AdaptThink, an adaptive thinking model.
2.  **Problem Generation**: A dedicated problem generator, DiffGen-8B, is trained on these selected difficult problems to produce new, challenging problems.
3.  **Solution Distillation and Filtration**: Long Chain-of-Thought (CoT) solutions for the newly generated problems are distilled using Qwen3-8B as a teacher model and then filtered for quality and relevance.

The final ScaleDiff-Math dataset combines these new problem-solution pairs with an original dataset to provide a more effective training signal. Evaluation was conducted on a suite of difficult mathematical benchmarks including AIME'24, AIME'25, HMMT-Feb'25, BRUMO'25, and MATH500.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- total_train_batch_size: 32
- total_eval_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.46.1
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3