New C-Guard Method Cuts AI Over-Refusal Rates by Half
markjeffrey · x · 2026-08-25
A new paper introduces C-Guard, a constitution-grid instrument for efficient RL alignment that resolves conflicting objectives in safety guard training. The method reduces over-refusal rates from 22.4% to 12.8% and utilizes a C-LIM score to prune ineffective data, boosting learning impact in specific regions from 0.733 to 0.80. The paper cites Bittensor's HaloGuard subnet as a foundational work alongside major AI safety labs. Code and the constitution are open-sourced.
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