How can graph diffusion models optimize automated bidding?
Multi-agent Auto-Bidding with Latent Graph Diffusion Models.
March 11, 2025
https://arxiv.org/pdf/2503.05805This paper introduces LGD-AB, a new method for automating bidding in large-scale ad auctions using a graph-based latent diffusion model. It models the complex interactions between ad opportunities and competing bidders as a graph, learning to predict auction outcomes and optimize bidding strategies under various constraints (KPIs like cost-per-acquisition, return on investment, etc.).
Key points for LLM-based multi-agent systems:
- Graph-based representation: Captures relationships between ad impressions and agents, enabling more nuanced modeling of the auction environment than traditional feature engineering. This approach can be generalized to other multi-agent scenarios where relationships between entities are crucial.
- Latent Diffusion Models (LDMs): Used to generate likely future auction trajectories, enabling planning-based bid optimization. This demonstrates the potential of LDMs for multi-agent planning and forecasting.
- Reward Alignment: Fine-tunes the LDM to align with desired KPIs using reinforcement learning and preference optimization techniques. This is essential for aligning LLM-based agents with specific goals in a multi-agent setting.
- Approximate Equilibrium Computation: Merges individual agent embeddings into a joint embedding space, promoting more coordinated bidding strategies. This concept can be explored for facilitating cooperation and communication between LLM-based agents.
- Knowledge Distillation: Used to train a smaller, more efficient agent model that mimics the performance of a larger, computationally expensive model trained with full information. This is relevant for deploying LLM-based agents in resource-constrained environments.