mlsanityData to 7 Oct 2026

AIGB: Generative Auto-bidding via Conditional Diffusion Modeling

arXiv 2405.16141 ↗First posted 25 May 2024cs.LG cs.AI cs.CEcited by 1

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

Auto-bidding plays a crucial role in facilitating online advertising by automatically providing bids for advertisers. Reinforcement learning (RL) has gained popularity for auto-bidding. However, most current RL auto-bidding methods are modeled through the Markovian Decision Process (MDP), which assumes the Markovian state transition. This assumption restricts the ability to perform in long horizon scenarios and makes the model unstable when dealing with highly random online advertising environments. To tackle this issue, this paper introduces AI-Generated Bidding (AIGB), a novel paradigm for auto-bidding through generative modeling. In this paradigm, we propose DiffBid, a conditional diffusion modeling approach for bid generation. DiffBid directly models the correlation between the return and the entire trajectory, effectively avoiding error propagation across time steps in long horizons. Additionally, DiffBid offers a versatile approach for generating trajectories that maximize given targets while adhering to specific constraints. Extensive experiments conducted on the real-world dataset and online A/B test on Alibaba advertising platform demonstrate the effectiveness of DiffBid, achieving 2.81% increase in GMV and 3.36% increase in ROI.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 23 of its 50 references that are in the map. First posted on arXiv 25 May 2024.

Builds on

23 of 50 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies20 Apr
  2. 2022Is Conditional Generative Modeling all you need for Decision-Making?28 Nov

Other references · 21

  1. 2022Classifier-Free Diffusion Guidancecited by 5,717
  2. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  3. 2018Group Normalizationcited by 1,514
  4. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,245
  5. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  6. 2022Diffusion Models in Vision: A Surveycited by 1,008
  7. 2015Variational Dropout and the Local Reparameterization Trickcited by 489
  8. 2022Offline Reinforcement Learning via High-Fidelity Generative Behavior Modelingcited by 177
  9. 2016Learning to Poke by Poking: Experiential Learning of Intuitive Physicscited by 152
  10. 2022FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesiscited by 136
  11. 2017Real-Time Bidding by Reinforcement Learning in Display Advertisingcited by 90
  12. 2018Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertisingcited by 65
9 more
  1. 2019Reinforcement Learning with Non-Markovian Rewardscited by 45
  2. 2022Denoising Likelihood Score Matching for Conditional Score-based Data Generationcited by 45
  3. 2016Display Advertising with Real-Time Bidding (RTB) and Behavioural Targetingcited by 42
  4. 2022The Importance of Non-Markovianity in Maximum State Entropy Explorationcited by 35
  5. 2022Sustainable Online Reinforcement Learning for Auto-biddingcited by 28
  6. 2021Robust Auction Design in the Auto-bidding Worldcited by 21
  7. 2021Towards Efficient Auctions in an Auto-bidding Worldcited by 19
  8. 2020Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertisingcited by 16
  9. 2023Learning non-Markovian Decision-Making from State-only Sequencescited by 6

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024A Survey on Diffusion Models for Recommender Systemscited by 20

Similar, not cited

Close in wording, no citation either way

  1. 2025Expert-Guided Diffusion Planner for Auto-Bidding
  2. 2025Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization
  3. 2026AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization
  4. 2025Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
  5. 2026Generative Auto-Bidding with Unified Modeling and Exploration
  6. 2026AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising
  7. 2026Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies
  8. 2026JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing