RBR-style scoring for Gauntlet Inspector findings #6
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coilyco-flight-deck/gauntlet#6
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Originally filed by @coilysiren on 2026-05-03T19:53:38Z - https://github.com/coilysiren/gauntlet/issues/7
Inspiration:
openai/safety-rbr-code-and-data- rule-based rewards: a small-data way to fit weights for policy-shaped LLM outputs.Joins against:
gauntlet's Inspector role (currently emits Findings as free-form text), the AI/ML literacy gap.Gauntlet's Inspector role is the obvious place for RBR. Findings today are unstructured text. Reframe as: each Finding scored against a small rule set (severity, blocker class, replayability) using the RBR weight-fitting code. The dataset ends up small enough to hand-label, the math ends up legible enough to write up, and the result is a real ML artifact tied to a real adversarial-testing system. AI/ML literacy gap closer that lives inside an existing repo, no new domain to learn.
Moved from coilysiren/coilyco-ai#40.
Iceboxed in the 2026-05-29 backlog burn-down: Explore-openai-RBR inspiration, speculative future feature. Reopen anytime if it becomes real.