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

arXiv: https://arxiv.org/abs/2609.08725 · HTML: https://mlsanity.com/paper/2609.08725/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Offline A/B testing for Recommender Systems](https://mlsanity.com/paper/1801.07030/) (2018)
- [Off-policy Bandits with Deficient Support](https://mlsanity.com/paper/2006.09438/) (2020)
- [Understanding Scaling Laws for Recommendation Models](https://mlsanity.com/paper/2208.08489/) (2022)
- [Reducing Symbiosis Bias Through Better A/B Tests of Recommendation Algorithms](https://mlsanity.com/paper/2309.07107/) (2023)
- [Cluster Randomized Designs for One-Sided Bipartite Experiments](https://mlsanity.com/paper/2210.16415/) (2022)
- [Causal Inference under Network Interference Using a Mixture of Randomized Experiments](https://mlsanity.com/paper/2309.00141/) (2023)
- [Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?](https://mlsanity.com/paper/2507.11891/) (2025)
- [Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach](https://mlsanity.com/paper/2310.17496/) (2023)
- [Interference Among First-Price Pacing Equilibria: A Bias and Variance Analysis](https://mlsanity.com/paper/2402.07322/) (2024)
- [Breaking Determinism: Stochastic Modeling for Reliable Off-Policy Evaluation in Ad Auctions](https://mlsanity.com/paper/2512.03354/) (2025)
- [Bias Mitigation for AI-Feedback Loops in Recommender Systems: A Systematic Literature Review and Taxonomy](https://mlsanity.com/paper/2509.00109/) (2025)

## Abstract (arXiv)

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. We formalize the Bid-Aware Filter Family (BAFF), a class of (k,l)-parameterized hard filters that controls tolerance to each channel independently, providing a structured search space between these two extremes. We further propose a three-stage online measurement protocol that enables evaluating data-sharing strategies by their deviation from an interference-free reference model in production. In offline simulation, a (k,l) sweep surfaces operating points with smaller deviation from the interference-free reference model than both log-sharing and log-splitting. In a live RTB deployment on a demand-side platform (DSP), filter-based variants preserve the reference model's business metrics (e.g., CPC, CTR) more closely than both baselines. The best operating point is setting-dependent, underscoring the practical value of the search space itself.