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newsReddit r/MachineLearningTrust 52 · CommunityPublished 9d agoLive · 9d ago

I audited 112 real RL post-training environments for reward-hacking vulnerabilities — 54 flagged, 0 false positives [OC, tool] [P]

RL post-training (RLHF/RLAIF/GRPO) agents optimize strictly for whatever the verifier rewards. If the verifier has logic flaws, the agent learns to hack the grader instead of solving the task — recent work has catalogued this at scale (Terminal Wrench found 331 hackable environments and 15%+ of standard benchmark tasks bypassable; a SWE-bench Verified audit found 28.5% Docker-verified hackability). I built ratctl , a static + dynamic auditor t

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