aiqbench analysis
This research introduces C-Guard, a novel framework designed to resolve conflicting objectives in reinforcement learning alignment by optimizing data efficiency without sacrificing safety or benign refusal rates. By utilizing per-cell learnability scores to prune dead-weight training regions before budget expenditure, the method significantly improves resource utilization while mitigating risks of over-refusal and silent failure against adversarial attacks.
A Constitution-Grid Instrument for Data-Efficient RL Alignment (C-Guard)
arXiv:2608.00180v1 Announce Type: new Abstract: Conflicting objectives are general in RL alignment, and training on them data-efficiently is hard. Training a safety guard with RL means optimizing two objectives that conflict: catch real harm, and do not refuse benign prompts. Our finding is that over-refusal improves 22.4% to 12.8%, while under-refusal on adversarial attacks silently worsens 0.27 to 0.33. We present C-Guard, a constitution-grid instrument that generates the RL training data, and C-LIM, a per-cell learnability score that decides each cell's move: prune, densify, amend, expand. C-LIM flags the dead-weight data region before any training budget is spent: 187 untargeted rows had bought zero gain, and our method lifts the same region's learning impact 0.733 to 0.80. Code and the constitution are open-sourced.
Abstract:Conflicting objectives are general in RL alignment, and training on them data-efficiently is hard. Training a safety guard with RL means optimizing two objectives that conflict: catch real harm, and do not refuse benign prompts. Our finding is that over-refusal improves 22.4% to 12.8%, while under-refusal on adversarial attacks silently worsens 0.27 to 0.33. We present C-Guard, a constitution-grid instrument that generates the RL training data, and C-LIM, a per-cell learnability score that decides each cell's move: prune, densify, amend, expand. C-LIM flags the dead-weight data region before any training budget is spent: 187 untargeted rows had bought zero gain, and our method lifts the same region's learning impact 0.733 to 0.80. Code and the constitution are open-sourced.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2608.00180 [cs.CL]
(or arXiv:2608.00180v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.00180 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 216 words
