arXiv:2605.26315v1 Announce Type: new Abstract: Direct Preference Optimisation (DPO) is widely used for safety alignment in large language models. However, prior work shows it is brittle and exhibits poor out-of-distribution (OOD) generalisation. In this paper, we investigate whether Curriculum Learning can improve the robustness of DPO-based safety alignment. We propose Staged-Competence, a curriculum-based framework that organises preference data by difficulty, employs competence-based sampling, and progressively updates the reference model during training. Averaged across three model famili
The paper addresses a critical, known vulnerability (brittleness and poor OOD generalization) in current safety alignment techniques for large language models, a rapidly evolving field.
Improving the robustness of safety alignment directly impacts the deployment and reliability of advanced AI systems, influencing their societal integration and regulatory frameworks.
This research introduces a method to potentially make AI safety alignment, particularly for DPO, more reliable and generalizable, reducing risks associated with unpredictable AI behavior.
- · AI developers
- · AI safety researchers
- · Cloud AI providers
- · AI-reliant industries
- · Malicious actors exploiting AI vulnerabilities
- · Legacy AI safety approaches
More robust and safer large language models become feasible for wider deployment.
Increased trust and adoption of AI technologies across various sectors due to enhanced safety guarantees.
Potentially, accelerated development of more powerful and autonomous AI agents capable of complex tasks with fewer oversight requirements.
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Read at arXiv cs.LG