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BAFF: Bid-Aware Filter Family for Mitigating Training Data Interference in RTB A/B Tests

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

In online A/B tests for real-time bidding (RTB), control and treatment models are typically trained on a shared serving log that includes data generated by the counterpart model. This shared-log training biases each model's training data through two channels: the counterpart model may have selected a different ad from the ad-candidate pool (ad-ranking disagreement) and may have bid a different price (bid-pricing disagreement), potentially distorting the A/B test outcome. Log-splitting eliminates the bias but sacrifices training data; log-sharing retains all data but leaves the bias unaddressed

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