InSPO: Enhancing LLM Alignment Through Self-Reflection
Published:Dec 29, 2025 00:59
•1 min read
•ArXiv
Analysis
This paper addresses limitations in existing preference optimization methods (like DPO) for aligning Large Language Models. It identifies issues with arbitrary modeling choices and the lack of leveraging comparative information in pairwise data. The proposed InSPO method aims to overcome these by incorporating intrinsic self-reflection, leading to more robust and human-aligned LLMs. The paper's significance lies in its potential to improve the quality and reliability of LLM alignment, a crucial aspect of responsible AI development.
Key Takeaways
- •InSPO is a novel method for aligning LLMs by incorporating intrinsic self-reflection.
- •It addresses limitations of DPO and its variants, such as sensitivity to modeling choices.
- •The method is designed to be a plug-and-play enhancement without architectural changes.
- •Experiments show improvements in win rates and length-controlled metrics, indicating better human alignment.
Reference
“InSPO derives a globally optimal policy conditioning on both context and alternative responses, proving superior to DPO/RLHF while guaranteeing invariance to scalarization and reference choices.”