Content moderation systems traditionally entangle multimodal understanding with policy-specific classification, requiring full pipeline retraining for every policy change and suffering from label scarcity since multimedia cannot be meaningfully augmented.
We propose Summarize-Judge-Refine (SJR), a two-model architecture that decouples these concerns via a natural language interface: a multimodal Content Model produces structured text summaries, and a text-only Policy Model classifies them against policy definitions.
An iterative co-training loop refines the Content Model via GRPO to produce policy-relevant summaries, while text-space augmentation generates adversarial summary variants---an augmentation pathway impossible on raw multimedia---enabling few-shot policy bootstrap.
Every decision is grounded in a human-readable summary, providing interpretability as a structural byproduct.
On misleading advertisement detection, SJR achieves +23.6% relative non-misleading F1 over a zero-shot chain-of-thought baseline, outperforming end-to-end SFT, STaR/RFT, and RLFT.
Notably, a variant trained on zero real violating examples---with all positive-class data synthetically generated---matches the full-data model within 0.2% relative on violating F1, demonstrating that new policies can launch without any real violation data.