mlsanityData to 14 Oct 2026

SEGB: Self-Evolved Generative Bidding with Local Autoregressive Diffusion

arXiv 2602.22226 ↗First posted 31 Dec 2025cs.IR cs.LGcited by 0

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Abstract (arXiv)

In the realm of online advertising, automated bidding has become a pivotal tool, enabling advertisers to efficiently capture impression opportunities in real-time. Recently, generative auto-bidding has shown significant promise, offering innovative solutions for effective ad optimization. However, existing offline-trained generative policies lack the near-term foresight required for dynamic markets and usually depend on simulators or external experts for post-training improvement. To overcome these critical limitations, we propose Self-Evolved Generative Bidding (SEGB), a framework that plans proactively and refines itself entirely offline. SEGB first synthesizes plausible short-horizon future states to guide each bid, providing the agent with crucial, dynamic foresight. Crucially, it then performs value-guided policy refinement to iteratively discover superior strategies without any external intervention. This self-contained approach uniquely enables robust policy improvement from static data alone. Experiments on the AuctionNet benchmark and a large-scale A/B test validate our approach, demonstrating that SEGB significantly outperforms state-of-the-art baselines. In a large-scale online deployment, it delivered substantial business value, achieving a +10.19% increase in target cost, proving the effectiveness of our advanced planning and evolution paradigm.

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18 of 43 references.

References · 18

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  4. 2022Generative Adversarial Networkscited by 5,627
  5. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  6. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  7. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,354
  8. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,293
  9. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
  10. 2024Autoregressive Image Generation without Vector Quantizationcited by 651
  11. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 644
  12. 2021Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemblecited by 302
6 more
  1. 2016Learning to Poke by Poking: Experiential Learning of Intuitive Physicscited by 154
  2. 2017Real-Time Bidding by Reinforcement Learning in Display Advertisingcited by 91
  3. 2018Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertisingcited by 65
  4. 2024AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Gamescited by 28
  5. 2023Multi-Task Recommendations with Reinforcement Learningcited by 26
  6. 2016Feedback Control of Real-Time Display Advertisingcited by 22

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