A research paper examining deliberative alignment for improving LLM safety, finding that alignment gaps persist between teacher and student models and that models can retain unsafe base behaviors. The authors propose Best-of-N (BoN) sampling that attributes unsafe responses back to base models in latent space, achieving safety improvements across multiple benchmarks.
Safety
Deliberative Alignment is Deep, but Uncertainty Remains: Inference time safety improvement in reasoning via attribution of unsafe behavior to base model
Best-of-N sampling at inference time catches unsafe behaviors that slip through alignment training by detecting base model signatures in latent space, improving safety across benchmarks.
Tuesday, April 14, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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safety