Beyond Uniform Forgetting: A Study of Sequential Direct Preference Optimization Across Preference Settings
arXiv:2606.19744v1 Announce Type: new Abstract: Aligning language models with human preferences often requires optimising multiple behavioural objectives. A practical approach is to apply these objectives sequentially using preference optimisation methods such as Direct Preference Optimisation (DPO), but it remains unclear whether later training uniformly degrades preferences learned earlier or whether the effect depends on the relationship between objectives. We study sequential DPO across four preference settings covering distributional conflict, multi-attribute interaction, strong safety si
The rapid advancement and deployment of large language models have necessitated more sophisticated alignment techniques to ensure they meet complex human preferences and safety standards.
Improving the alignment of AI models with human preferences directly impacts their safety, utility, and trustworthiness, which are critical for widespread adoption and societal integration.
This research provides a deeper understanding of how sequential optimization affects AI model behavior, enabling more effective and robust alignment strategies for complex, multi-objective scenarios.
- · AI developers
- · Generative AI platforms
- · AI safety researchers
- · AI-powered product companies
- · Developers of unaligned AI models
- · Companies relying on primitive AI alignment methods
More reliable and safer AI models are developed, leading to higher user adoption and trust.
Advanced alignment techniques become a competitive advantage, favoring companies with strong AI research capabilities.
The increased sophistication of AI alignment could contribute to the development of more autonomous and agentic AI systems that consistently adhere to complex ethical guidelines.
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Read at arXiv cs.CL