mlsanityData to 14 Oct 2026

Expert-Guided Diffusion Planner for Auto-Bidding

arXiv 2508.08687 ↗First posted 12 Aug 2025cs.LG cs.IRcited by 2

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

Abstract (arXiv)

Auto-bidding is widely used in advertising systems, serving a diverse range of advertisers. Generative bidding is increasingly gaining traction due to its strong planning capabilities and generalizability. Unlike traditional reinforcement learning-based bidding, generative bidding does not depend on the Markov Decision Process (MDP), thereby exhibiting superior planning performance in long-horizon scenarios. Conditional diffusion modeling approaches have shown significant promise in the field of auto-bidding. However, relying solely on return as the optimality criterion is insufficient to guarantee the generation of truly optimal decision sequences, as it lacks personalized structural information. Moreover, the auto-regressive generation mechanism of diffusion models inherently introduces timeliness risks. To address these challenges, we introduce a novel conditional diffusion modeling approach that integrates expert trajectory guidance with a skip-step sampling strategy to improve generation efficiency. The efficacy of this method has been demonstrated through comprehensive offline experiments and further substantiated by statistically significant outcomes in online A/B testing, yielding an 11.29% increase in conversions and a 12.36% growth in revenue relative to the baseline.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 26 of its 39 references that are in the map. First posted on arXiv 12 Aug 2025.

Builds on

26 of 39 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Diffusion Models for Reinforcement Learning: A Survey2 Nov
  2. 2023IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies20 Apr

Other references · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  4. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  5. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,293
  6. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
  7. 2015Scheduled Sampling for Sequence Prediction with Recurrent Neural Networkscited by 803
  8. 2018Behavioral Cloning from Observationcited by 492
  9. 2022Offline Reinforcement Learning via High-Fidelity Generative Behavior Modelingcited by 182
  10. 2016Learning to Poke by Poking: Experiential Learning of Intuitive Physicscited by 154
  11. 2023DiffuRec: A Diffusion Model for Sequential Recommendationcited by 93
  12. 2017Real-Time Bidding by Reinforcement Learning in Display Advertisingcited by 91
12 more
  1. 2023Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusioncited by 74
  2. 2018Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertisingcited by 65
  3. 2024Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theorycited by 63
  4. 2016Display Advertising with Real-Time Bidding (RTB) and Behavioural Targetingcited by 42
  5. 2013Real-time Bidding for Online Advertising: Measurement and Analysiscited by 32
  6. 2022Sustainable Online Reinforcement Learning for Auto-biddingcited by 29
  7. 2023Safe Offline Reinforcement Learning with Real-Time Budget Constraintscited by 28
  8. 2024Auto-bidding and Auctions in Online Advertising: A Surveycited by 26
  9. 2025Generative Auto-Bidding with Value-Guided Explorationscited by 23
  10. 2021A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertisingcited by 22
  11. 2024Out-of-Distribution Detection with a Single Unconditional Diffusion Modelcited by 22
  12. 2024Dual Conditional Diffusion Models for Sequential Recommendationcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricingcited by 0
  2. 2026Generative Auto-Bidding with Unified Modeling and Explorationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024AIGB: Generative Auto-bidding via Conditional Diffusion Modeling
  2. 2026AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising
  3. 2025Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
  4. 2025Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization
  5. 2026AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization
  6. 2026Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies
  7. 2025Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement
  8. 2026DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation