AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models
arXiv:2509.25148v2 Announce Type: replace Abstract: Post-training alignment of large language models often combines supervised fine-tuning (SFT) on expert demonstrations with reinforcement learning (RL) from preference or verifiable feedback. SFT provides a useful behavioral anchor but can overfit to static demonstrations, whereas RL encourages exploration but may drift from expert behavior or exploit imperfect rewards. We propose \textbf{AAPA} (\emph{Adversarially Anchored Preference Alignment}), a plug-in framework that augments existing post-training objectives with a sentence-level adversa
The ongoing rapid development and deployment of large language models necessitates continuous innovation in alignment techniques to enhance their performance, safety, and ethical behavior.
This work introduces a novel, plug-in framework for post-training alignment that promises more robust and reliable AI models, directly impacting the quality and capabilities of advanced AI systems.
The proposed AAPA method offers a way to combine the benefits of supervised fine-tuning and reinforcement learning more effectively, potentially leading to faster and more stable development of high-performing LLMs.
- · AI researchers and developers
- · Companies deploying LLM-based products
- · Users of advanced AI applications
- · Organizations relying on less robust alignment techniques
Improved performance and reliability of large language models.
Accelerated innovation in AI applications due to more predictable and capable base models.
Enhanced trust and broader adoption of AI across sensitive domains, contingent on sustained alignment progress.
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Read at arXiv cs.AI