Generative Auto-Bidding with Unified Modeling and Exploration

2026-05-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, E-commerce & Digital Commerce · Depth: Expert, extended

Summary

The Generative Auto-Bidding with Unified Modeling and Exploration (Guide) framework addresses the critical challenge of balancing exploration and safety in digital advertising's automated bidding systems. Guide integrates a Decision Transformer (DT) to model historical bidding actions and environmental state transitions, a Q-value module that guides DT's exploration through regularization, and an Inverse Dynamics Module (IDM) which infers robust actions from DT-predicted future states, serving as a safety fallback. The Q-value module then adaptively selects the final action, unifying an "explore–safeguard–select" pipeline. Comprehensive experiments, including a large-scale online deployment on Taobao, demonstrate Guide's superior performance, achieving +4.10% in ad GMV, +1.40% in ad clicks, +1.66% in ad cost, and +3.52% in ad ROI, consistently outperforming state-of-the-art baselines.

Key takeaway

For AI Scientists and ML Engineers developing auto-bidding systems, Guide presents a robust framework to enhance ad campaign performance and manage financial risk. You should consider its "explore–safeguard–select" architecture, which adaptively balances aggressive exploration with a safe fallback. Implementing this approach can lead to significant gains, as demonstrated by +3.52% ad ROI and +4.10% ad GMV on Taobao, ensuring more intelligent budget allocation and higher-quality traffic acquisition.

Key insights

Guide unifies exploration and safety in auto-bidding by combining generative modeling with a robust fallback mechanism.

Principles

• Jointly modeling environmental dynamics and bidding actions deepens system understanding.
• An "explore–safeguard–select" pipeline effectively balances risk and discovery.
• Two-stage training stabilizes and accelerates learning for complex generative models.

Method

Guide employs a Decision Transformer for joint action/state prediction, an Inverse Dynamics Module for safe fallback actions, and a Q-value module for exploration guidance and adaptive action selection.

In practice

• Integrate Decision Transformers for sequence modeling in dynamic ad environments.
• Utilize an Inverse Dynamics Module to provide stable, conservative fallback actions.
• Apply Q-value regularization to direct generative model exploration.

Topics

• Auto Bidding
• Decision Transformer
• Generative Models
• Inverse Dynamics Model
• Q-value Optimization
• Digital Advertising

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Director of AI/ML

Related on AIssential

• Generative Auto-Bidding with Unified Modeling and Exploration — GUIDE unifies exploration and safety in generative auto-bidding using a Decision Transformer, Q-value guidance, and an Inverse Dynamics Module.
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.