WARP Framework Recovers Training Data Portfolios from Model Weights

WARP utilizes model merging to interpolate between base and fine-tuned weights, creating pseudo-checkpoints to reconstruct training data mixtures. Unlike membership inference, this framework allows for the recovery of global training distribution characteristics and domain mixture weights from released model weights.