# RTBAgent: A LLM-based Agent System for Real-Time Bidding

arXiv: https://arxiv.org/abs/2502.00792 · HTML: https://mlsanity.com/paper/2502.00792/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 2 Feb 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 15 of 15)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Self-Rewarding Language Models](https://mlsanity.com/paper/2401.10020/) (2024)
- [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023)
- [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024)
- [HuatuoGPT, towards Taming Language Model to Be a Doctor](https://mlsanity.com/paper/2305.15075/) (2023)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations](https://mlsanity.com/paper/2311.00273/) (2023)
- [Online Convex Optimization with Stochastic Constraints](https://mlsanity.com/paper/1708.03741/) (2017)
- [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)
- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [EduAgent: Generative Student Agents in Learning](https://mlsanity.com/paper/2404.07963/) (2024)
- [Deep Reinforcement Learning for Sponsored Search Real-time Bidding](https://mlsanity.com/paper/1803.00259/) (2018)
- [Optimal Real-Time Bidding Frameworks Discussion](https://mlsanity.com/paper/1602.01007/) (2016)

## Built on by (most cited first, 14 of 14)

- [Agent Exchange: Shaping the Future of AI Agent Economics](https://mlsanity.com/paper/2507.03904/) (2025)
- [CombatVLA: An Efficient Vision-Language-Action Model for Combat Tasks in 3D Action Role-Playing Games](https://mlsanity.com/paper/2503.09527/) (2025)
- [Dynamic Speculative Agent Planning](https://mlsanity.com/paper/2509.01920/) (2025)
- [MVISU-Bench: Benchmarking Mobile Agents for Real-World Tasks by Multi-App, Vague, Interactive, Single-App and Unethical Instructions](https://mlsanity.com/paper/2508.09057/) (2025)
- [CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Models](https://mlsanity.com/paper/2512.07890/) (2025)
- [Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making](https://mlsanity.com/paper/2508.15926/) (2025)
- [Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization](https://mlsanity.com/paper/2508.02002/) (2025)
- [Evolving Deception: When Agents Evolve, Deception Wins](https://mlsanity.com/paper/2603.05872/) (2026)
- [EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus](https://mlsanity.com/paper/2510.12899/) (2025)
- [Information-Dense Reasoning for Efficient and Auditable Security Alert Triage](https://mlsanity.com/paper/2512.08169/) (2025)
- [Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies](https://mlsanity.com/paper/2601.02754/) (2026)
- [KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits](https://mlsanity.com/paper/2601.05257/) (2025)
- [Generative Auto-Bidding with Unified Modeling and Exploration](https://mlsanity.com/paper/2605.19457/) (2026)
- [AdCoEvo: A Co-Evolving Multi-Agent Advertising Delivery System](https://mlsanity.com/paper/2610.19454/) (2026)

## Abstract (arXiv)

Real-Time Bidding (RTB) enables advertisers to place competitive bids on impression opportunities instantaneously, striving for cost-effectiveness in a highly competitive landscape. Although RTB has widely benefited from the utilization of technologies such as deep learning and reinforcement learning, the reliability of related methods often encounters challenges due to the discrepancies between online and offline environments and the rapid fluctuations of online bidding. To handle these challenges, RTBAgent is proposed as the first RTB agent system based on large language models (LLMs), which synchronizes real competitive advertising bidding environments and obtains bidding prices through an integrated decision-making process. Specifically, obtaining reasoning ability through LLMs, RTBAgent is further tailored to be more professional for RTB via involved auxiliary modules, i.e., click-through rate estimation model, expert strategy knowledge, and daily reflection. In addition, we propose a two-step decision-making process and multi-memory retrieval mechanism, which enables RTBAgent to review historical decisions and transaction records and subsequently make decisions more adaptive to market changes in real-time bidding. Empirical testing with real advertising datasets demonstrates that RTBAgent significantly enhances profitability. The RTBAgent code will be publicly accessible at: https://github.com/CaiLeng/RTBAgent.