Constitutional adapters: Inference-time interventions for misalignment and misuse

Abstract:Training models to act in accordance with an explicitly defined set of principles, or "constitution," has shown promise as a robust and transparent mechanism for AI alignment. However, the generality and flexibility of such methods remain unclear. Here, we show that constitution-consistent behavior can be distilled from synthetic corpora into lightweight objects (low-rank adapters and steering vectors). Despite never seeing a harmful request or jailbreak during training, such objects increase jailbreak defense success and measured alignment -- particularly at long context lengths and against multi-turn attacks, where they outperform both prompted and steered baselines. Subtracting control-trained from constitution-trained objects further accentuates these effects, yielding defenses we call "constitutional adapters" (CAs). CAs can be trained on a base model, transferred zero-shot to its post-trained checkpoint, and scaled at inference time to predictably trade off defense for benign compliance. Taken together, these results recommend CAs as a lightweight, portable, and tunable lever for mitigating misalignment and misuse in API deployments.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.36657 [cs.LG]

(or arXiv:2609.36657v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.36657 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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