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UC Davis
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Iterative local refinement in Mask Diffusion Models can outperform traditional autoregressive methods, transforming how we approach reasoning in AI.
PRIME reveals a crucial precursor to reward hacking that can predict and adapt to misalignment before it manifests, offering a new lens on alignment risks in RL systems.
