arXiv:2606.03089v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as an efficient post-training paradigm by using a teacher conditioned on privileged information to provide dense token-level supervision. Prior work has shown that OPSD can collapse in verifiable reasoning tasks, but safety alignment differs in that it is guided by high-level constitutions rather than explicit target answers, making it a natural setting to revisit dense distillation. However, our pilot study show that safety OPSD still suffers from severe collapse: constitutional conditioning contrac
This research addresses a critical challenge in AI safety at a time when 'constitutional AI' and self-distillation methods are being actively explored for robust and ethical AI development.
Ensuring AI models adhere to safety guidelines without collapsing performance is paramount for their widespread deployment and acceptance, impacting future AI product viability and regulatory frameworks.
This research highlights the limitations of current on-policy safe distillation techniques, indicating that methods for reliable AI safety alignment still require significant advancements.
- · AI Safety Researchers
- · Developers working on safer AI systems
- · Companies relying on naive self-distillation for safety
- · Efforts for quick AI safety scaling
The finding complicates the path to deploying robustly safe AI, especially large language models.
It could lead to increased investment in novel AI alignment techniques beyond current self-distillation paradigms.
Delayed deployment of certain AI applications due to unresolved safety and ethical concerns may occur, impacting industry timelines.
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Read at arXiv cs.LG