---
library_name: transformers
license: apache-2.0
license_link: https://github.com/foreverlasting1202/QuestA/blob/main/LICENSE
pipeline_tag: text-generation

---

# QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation

<p align="center">
  | <a href="https://www.arxiv.org/abs/2507.13266"><b>Paper</b></a> | <a href="https://github.com/foreverlasting1202/QuestA/"><b>Documentation</b></a> | <a
href="https://mercurial-kidney-02d.notion.site/QuestA-Expanding-Reasoning-Capacity-in-LLMs-via-Question-Augmentation-216b21d08abb81a1bcecfe79e7d1e88a"><b>Blog</b></a> | <a
href="https://huggingface.co/foreverlasting1202/QuestA-Nemotron-1.5B"><b>🤗Models</b></a> | <a
href="https://huggingface.co/datasets/foreverlasting1202/QuestA"><b>🤗Datas</b></a> | <a
</p>

## Highlights

QuestA introduces **question augmentation** to significantly improve reasoning tasks in large language models (LLMs). By incorporating partial solutions during reinforcement learning (RL) training, QuestA enhances problem-solving capacity and accelerates learning on challenging tasks. Key improvements with **QuestA**:

- **Significant performance boost** on math reasoning benchmarks (e.g., AIME25, HMMT25), including a **10%+ increase** in accuracy.
- **Enhanced training efficiency** via augmented prompts, allowing more tractable learning on hard problems.
- **State-of-the-art results** for 1.5B-parameter models, making QuestA effective even on models with smaller parameter sizes.


![benchmarks](https://cdn-uploads.huggingface.co/production/uploads/6740b458116d17ed148be1e3/JUT4yQ_U5Wm8A7c_at1px.png)

![pass_full](https://cdn-uploads.huggingface.co/production/uploads/6740b458116d17ed148be1e3/Ix-WKPAFScqmzC83Uz6qw.png)

## Model Overview

- **Model Type**: Causal Language Model (RL-based Training)
- **Training Method**: Reinforcement Learning (RL) with Question Augmentation
- **Number of Parameters**: 1.5B (base model), augmented with dynamic difficulty control
- **Layer Count**: Customizable based on the RL training configuration
- **Context Length**: 32K tokens (configurable)
- **Main Innovation**: Question Augmentation with Partial Solutions

QuestA dynamically adjusts problem difficulty by providing partial solutions to complex problems, thus improving the model’s ability to solve hard tasks more effectively.

## Performance

QuestA achieves the following performance improvements over baseline models, particularly in the field of math reasoning:

| Model                   | AIME24   | AIME25   | HMMT FEB 25 | Olympiad Bench | BRUMO25  | Avg.     |
| ----------------------- | -------- | -------- | ----------- | -------------- | -------- | -------- |
| DeepSeek-R1-Distill-32B | **72.6** | 51.8     | 33.0        | 65.0           | 68.0     | 58.1     |
| Qwen3-1.7B              | 48.3     | 36.8     | 22.2        | 56.1           | 44.1     | 41.5     |
| Nemotron-1.5B           | 61.8     | 49.5     | 31.6        | 64.6           | 58.2     | 53.1     |
| *QuestA*-Nemotron-1.5B  | 72.5     | **62.3** | **41.7**    | **70.4**       | **69.5** | **63.3** |

- **Pass@k Performance**: Shows consistent improvement across various difficulty levels.

## Quickstart

To get started with QuestA, you can load the model using the `transformers` library. Make sure you have the latest version installed.

```bash
pip install transformers
```

Example Python code to run QuestA:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "QuestA/QuestA-Nemotron-1.5B"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate response with augmented question
prompt = "Solve for x: 2x + 3 = 11."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)

# Decode the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

For deployment, QuestA can be served using frameworks like **vLLM** or **SGLang**:

```bash
# For vLLM
vllm serve QuestA/QuestA-Nemotron-1.5B --tensor-parallel-size 8 --max-model-len 32768
```

## Key Features

- **Question Augmentation**: Prepend partial solutions to difficult problems, aiding model learning.
- **Curriculum-based RL**: Gradually reduce dependency on hints as training progresses.
- **Training with Augmented Data**: Use dynamically filtered datasets to focus on the hardest problems.
- **Efficient Learning**: Faster convergence on complex tasks due to better sampling and more informative rewards.

## Citation

If you find this work useful, please cite our paper:

```bibtex
@misc{li2025questaexpandingreasoningcapacity,
      title={QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation}, 
      author={Jiazheng Li and Hong Lu and Kaiyue Wen and Zaiwen Yang and Jiaxuan Gao and Hongzhou Lin and Yi Wu and Jingzhao Zhang},
      year={2025},
      eprint={2507.13266},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.13266}, 
}
```

For more details on the methodology, results, and code, visit the official [QuestA GitHub repository](https://github.com/foreverlasting1202/QuestA).

## Conclusion

QuestA is a novel framework for enhancing LLMs' reasoning capabilities by addressing complex problems more effectively. By augmenting the training process with partial solutions, QuestA accelerates learning, resulting in state-of-the-art performance on benchmark math reasoning tasks and more.