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

arXiv: https://arxiv.org/abs/2502.05187 · HTML: https://mlsanity.com/paper/2502.05187/

## 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 (most cited first, 20 of 20)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [In-context Reinforcement Learning with Algorithm Distillation](https://mlsanity.com/paper/2210.14215/) (2022)
- [Prompting Decision Transformer for Few-Shot Policy Generalization](https://mlsanity.com/paper/2206.13499/) (2022)
- [Supervised Pretraining Can Learn In-Context Reinforcement Learning](https://mlsanity.com/paper/2306.14892/) (2023)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Deep Reinforcement Learning for Sponsored Search Real-time Bidding](https://mlsanity.com/paper/1803.00259/) (2018)
- [Sustainable Online Reinforcement Learning for Auto-bidding](https://mlsanity.com/paper/2210.07006/) (2022)
- [Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising](https://mlsanity.com/paper/2103.10246/) (2021)
- [Feedback Control of Real-Time Display Advertising](https://mlsanity.com/paper/1603.01055/) (2016)
- [Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns](https://mlsanity.com/paper/2003.01452/) (2020)
- [Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising](https://mlsanity.com/paper/2006.16312/) (2020)
- [Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing](https://mlsanity.com/paper/2303.01049/) (2023)
- [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://mlsanity.com/paper/2107.06650/) (2021)
- [Multi-channel Autobidding with Budget and ROI Constraints](https://mlsanity.com/paper/2302.01523/) (2023)
- [Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search](https://mlsanity.com/paper/2106.04075/) (2021)
- [Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets](https://mlsanity.com/paper/2306.07709/) (2023)
- [HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2312.17503/) (2023)

## Built on by (most cited first, 5 of 5)

- [DARA: Few-shot Budget Allocation in Online Advertising via In-Context Decision Making with RL-Finetuned LLMs](https://mlsanity.com/paper/2601.14711/) (2026)
- [Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search](https://mlsanity.com/paper/2509.15927/) (2025)
- [PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding](https://mlsanity.com/paper/2602.08261/) (2026)
- [A Modality-Aware Cooperative Co-Evolutionary Framework for Multimodal Graph Neural Architecture Search](https://mlsanity.com/paper/2510.07325/) (2025)
- [AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising](https://mlsanity.com/paper/2602.22650/) (2026)

## 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.