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Model: Jingleqian/AAPA-8B
Source: Original Platform

README.md

@@ -0,0 +1,54 @@
+---
+base_model: Qwen/Qwen3-8B
+language:
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+datasets:
+- Jingleqian/AAPA-data
+tags:
+- alignment
+- grpo
+- aapa
+- qwen
+- instruction-following
+---
+
+# AAPA-8B
+
+This repository contains the 8B A-GRPO checkpoint released with **AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models**.
+
+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.
+
+This checkpoint is trained from [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) using the AAPA code release.
+
+## Resources
+
+- Paper: https://arxiv.org/abs/2509.25148
+- Hugging Face Paper: https://huggingface.co/papers/2509.25148
+- Code: https://github.com/IsFaqq/AAPA
+- Data: https://huggingface.co/datasets/Jingleqian/AAPA-data
+- 0.6B checkpoint: https://huggingface.co/Jingleqian/AAPA-06B
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Jingleqian/AAPA-8B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+```
+
+## Citation
+
+```bibtex
+@article{aapa2025,
+  title={AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models},
+  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},
+  journal={arXiv preprint arXiv:2509.25148},
+  year={2025}
+}
+```