55 lines
1.6 KiB
Markdown
55 lines
1.6 KiB
Markdown
---
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base_model: Qwen/Qwen3-0.6B
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language:
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- en
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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datasets:
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- Jingleqian/AAPA-data
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tags:
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- alignment
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- grpo
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- aapa
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- qwen
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- instruction-following
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---
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# AAPA-06B
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This repository contains the 0.6B A-GRPO checkpoint released with **AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models**.
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AAPA is a plug-in framework that augments post-training objectives with a sentence-level adversarial anchoring signal. It compares policy rollouts with offline expert responses using a fixed lightweight discriminator, providing semantic grounding during preference optimization.
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This checkpoint is trained from [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) using the AAPA code release.
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## Resources
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- Paper: https://arxiv.org/abs/2509.25148
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- Hugging Face Paper: https://huggingface.co/papers/2509.25148
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- Code: https://github.com/IsFaqq/AAPA
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- Data: https://huggingface.co/datasets/Jingleqian/AAPA-data
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- 8B checkpoint: https://huggingface.co/Jingleqian/AAPA-8B
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "Jingleqian/AAPA-06B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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```
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## Citation
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```bibtex
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@article{aapa2025,
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title={AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models},
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author={Faqiang Qian and Kang An and Weikun Zhang and Ziliang Wang and Xuhui Zheng and Liangjian Wen and Yong Dai and Mengya Gao and Yichao Wu},
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journal={arXiv preprint arXiv:2509.25148},
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year={2025}
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}
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```
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