C-Instrument: Automating RL Data Generation and Hillclimbing with a Constitution-Grid Instrument
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-Instrument, a constitution-grid data 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 l...
| Authors | Xianling Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 31 July 2026 |
| Last updated at source | 29 September 2026 |
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