Reinforcement Learning for Controller Parameters in the Auto-Bidding System at Pinterest

Computer Science > Machine Learning

[Submitted on 23 Oct 2026]

Title:Reinforcement Learning for Controller Parameters in the Auto-Bidding System at Pinterest

View PDF HTML (experimental)Abstract:Auto-bidding systems in online advertising must balance competing objectives-maximizing budget delivery while minimizing cost per action-across heterogeneous campaigns under non-stationary market conditions. This trade-off evolves over campaign lifespans: early on, cost optimization dominates when budgets roll over; near deadlines, budget delivery becomes urgent. While PID controllers are widely used in production for their stability and interpretability, fixed tunings cannot adapt to diverse campaigns and shifting traffic patterns. We present RLBP (Reinforcement Learning for Bidding Parameters), a hybrid architecture deployed at Pinterest that uses RL as a meta-controller to select campaign-specific PID parameters daily while retaining PID for high-frequency bid adjustments. RLBP trains a multi-head Q-network using a relative-lift reward formulation normalized against the production baseline, and performs inference-time fusion that adapts the cost-delivery trade-off to campaign states without retraining. Through online A/B testing at Pinterest, RLBP achieves a statistically significant reduction in effective cost-per-click (eCPC) with no revenue degradation, and two months of post-rollout measurements against a holdout also show consistent cost reductions. These results demonstrate that meta-controller RL can successfully augment production control systems at scale.

Additional Features

References & Citations

Loading...

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)

Connected Papers (What is Connected Papers?)

Litmaps (What is Litmaps?)

scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub (What is DagsHub?)

Gotit.pub (What is GotitPub?)

Hugging Face (What is Huggingface?)

ScienceCast (What is ScienceCast?)

Demos

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)

CORE Recommender (What is CORE?)

IArxiv Recommender

(What is IArxiv?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.