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Model: foreverlasting1202/QuestA-Nemotron-1.5B Source: Original Platform
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---
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library_name: transformers
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license: apache-2.0
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license_link: https://github.com/foreverlasting1202/QuestA/blob/main/LICENSE
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pipeline_tag: text-generation
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---
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# QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
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<p align="center">
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| <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
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href="https://mercurial-kidney-02d.notion.site/QuestA-Expanding-Reasoning-Capacity-in-LLMs-via-Question-Augmentation-216b21d08abb81a1bcecfe79e7d1e88a"><b>Blog</b></a> | <a
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href="https://huggingface.co/foreverlasting1202/QuestA-Nemotron-1.5B"><b>🤗Models</b></a> | <a
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href="https://huggingface.co/datasets/foreverlasting1202/QuestA"><b>🤗Datas</b></a> | <a
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</p>
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## Highlights
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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**:
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- **Significant performance boost** on math reasoning benchmarks (e.g., AIME25, HMMT25), including a **10%+ increase** in accuracy.
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- **Enhanced training efficiency** via augmented prompts, allowing more tractable learning on hard problems.
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- **State-of-the-art results** for 1.5B-parameter models, making QuestA effective even on models with smaller parameter sizes.
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## Model Overview
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- **Model Type**: Causal Language Model (RL-based Training)
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- **Training Method**: Reinforcement Learning (RL) with Question Augmentation
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- **Number of Parameters**: 1.5B (base model), augmented with dynamic difficulty control
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- **Layer Count**: Customizable based on the RL training configuration
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- **Context Length**: 32K tokens (configurable)
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- **Main Innovation**: Question Augmentation with Partial Solutions
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QuestA dynamically adjusts problem difficulty by providing partial solutions to complex problems, thus improving the model’s ability to solve hard tasks more effectively.
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## Performance
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QuestA achieves the following performance improvements over baseline models, particularly in the field of math reasoning:
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| Model | AIME24 | AIME25 | HMMT FEB 25 | Olympiad Bench | BRUMO25 | Avg. |
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| ----------------------- | -------- | -------- | ----------- | -------------- | -------- | -------- |
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| DeepSeek-R1-Distill-32B | **72.6** | 51.8 | 33.0 | 65.0 | 68.0 | 58.1 |
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| Qwen3-1.7B | 48.3 | 36.8 | 22.2 | 56.1 | 44.1 | 41.5 |
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| Nemotron-1.5B | 61.8 | 49.5 | 31.6 | 64.6 | 58.2 | 53.1 |
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| *QuestA*-Nemotron-1.5B | 72.5 | **62.3** | **41.7** | **70.4** | **69.5** | **63.3** |
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- **Pass@k Performance**: Shows consistent improvement across various difficulty levels.
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## Quickstart
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To get started with QuestA, you can load the model using the `transformers` library. Make sure you have the latest version installed.
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```bash
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pip install transformers
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```
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Example Python code to run QuestA:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "QuestA/QuestA-Nemotron-1.5B"
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Generate response with augmented question
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prompt = "Solve for x: 2x + 3 = 11."
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs)
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# Decode the response
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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For deployment, QuestA can be served using frameworks like **vLLM** or **SGLang**:
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```bash
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# For vLLM
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vllm serve QuestA/QuestA-Nemotron-1.5B --tensor-parallel-size 8 --max-model-len 32768
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```
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## Key Features
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- **Question Augmentation**: Prepend partial solutions to difficult problems, aiding model learning.
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- **Curriculum-based RL**: Gradually reduce dependency on hints as training progresses.
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- **Training with Augmented Data**: Use dynamically filtered datasets to focus on the hardest problems.
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- **Efficient Learning**: Faster convergence on complex tasks due to better sampling and more informative rewards.
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## Citation
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If you find this work useful, please cite our paper:
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```bibtex
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@misc{li2025questaexpandingreasoningcapacity,
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title={QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation},
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author={Jiazheng Li and Hong Lu and Kaiyue Wen and Zaiwen Yang and Jiaxuan Gao and Hongzhou Lin and Yi Wu and Jingzhao Zhang},
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year={2025},
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eprint={2507.13266},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2507.13266},
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}
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```
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For more details on the methodology, results, and code, visit the official [QuestA GitHub repository](https://github.com/foreverlasting1202/QuestA).
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## Conclusion
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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.
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|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n{%- else %}\n {{- '<|im_start|>system\n<|im_end|>\n' }}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == 'user') or (message.role == 'system' and not loop.first) or (message.role == 'assistant') %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user