mlsanityData to 14 Oct 2026

An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising

arXiv 2502.05187 ↗First posted 26 Jan 2025cs.GT cs.LGcited by 5

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Abstract (arXiv)

In online advertising, advertisers commonly utilize auto-bidding services to bid for impression opportunities. A typical objective of the auto-bidder is to optimize the advertiser's cumulative value of winning impressions within specified budget constraints. However, such a problem is challenging due to the complex bidding environment faced by diverse advertisers. To address this challenge, we introduce ABPlanner, a few-shot adaptable budget planner designed to improve budget-constrained auto-bidding. ABPlanner is based on a hierarchical bidding framework that decomposes the bidding process into shorter, manageable stages. Within this framework, ABPlanner allocates the budget across all stages, allowing a low-level auto-bidder to bids based on the budget allocation plan. The adaptability of ABPlanner is achieved through a sequential decision-making approach, inspired by in-context reinforcement learning. For each advertiser, ABPlanner adjusts the budget allocation plan episode by episode, using data from previous episodes as prompt for current decisions. This enables ABPlanner to quickly adapt to different advertisers with few-shot data, providing a sample-efficient solution. Extensive simulation experiments and real-world A/B testing validate the effectiveness of ABPlanner, demonstrating its capability to enhance the cumulative value achieved by auto-bidders.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 26 Jan 2025.

Builds on

20 of 41 references.

References · 20

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  3. 2022In-context Reinforcement Learning with Algorithm Distillationcited by 190
  4. 2022Prompting Decision Transformer for Few-Shot Policy Generalizationcited by 146
  5. 2023Supervised Pretraining Can Learn In-Context Reinforcement Learningcited by 139
  6. 2017Real-Time Bidding by Reinforcement Learning in Display Advertisingcited by 91
  7. 2018Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertisingcited by 61
  8. 2019Bid Optimization by Multivariable Control in Display Advertisingcited by 37
  9. 2018Deep Reinforcement Learning for Sponsored Search Real-time Biddingcited by 34
  10. 2022Sustainable Online Reinforcement Learning for Auto-biddingcited by 29
  11. 2021Stochastic Bandits for Multi-platform Budget Optimization in Online Advertisingcited by 25
  12. 2016Feedback Control of Real-Time Display Advertisingcited by 22
8 more
  1. 2020Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaignscited by 16
  2. 2020Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertisingcited by 16
  3. 2023Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketingcited by 15
  4. 2021An Efficient Deep Distribution Network for Bid Shading in First-Price Auctionscited by 14
  5. 2023Multi-channel Autobidding with Budget and ROI Constraintscited by 14
  6. 2021Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Searchcited by 7
  7. 2023Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgetscited by 4
  8. 2023HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learningcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2026DARA: Few-shot Budget Allocation in Online Advertising via In-Context Decision Making with RL-Finetuned LLMscited by 4
  2. 2025Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Searchcited by 1
  3. 2026PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Biddingcited by 1
  4. 2025A Modality-Aware Cooperative Co-Evolutionary Framework for Multimodal Graph Neural Architecture Searchcited by 0
  5. 2026AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertisingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Offline Reinforcement Learning for Optimizing Production Bidding Policies
  2. 2024Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)
  3. 2026AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization
  4. 2024AIGB: Generative Auto-bidding via Conditional Diffusion Modeling
  5. 2025Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization
  6. 2026Learning to Allocate Incentives for Incentivized Advertising via Offline Model-Based Reinforcement Learning
  7. 2026OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios
  8. 2026HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising