# Marrying Pricing and Advertising with LLMs

arXiv: https://arxiv.org/abs/2610.09985 · HTML: https://mlsanity.com/paper/2610.09985/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 5 of 5)

- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Improving Generative Ad Text on Facebook using Reinforcement Learning](https://mlsanity.com/paper/2507.21983/) (2025)
- [CTR-Driven Ad Text Generation via Online Feedback Preference Optimization](https://mlsanity.com/paper/2507.20227/) (2025)
- [Deep Learning for Individual Heterogeneity](https://mlsanity.com/paper/2010.14694/) (2020)
- [RELATE: A Reinforcement Learning-Enhanced LLM Framework for Advertising Text Generation](https://mlsanity.com/paper/2602.11780/) (2026)

## Abstract (arXiv)

We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The seller aims to maximize revenue under an unknown product demand that depends on both decisions, while observing only whether each offer leads to a purchase. We propose an online actor-critic algorithm that combines low-rank adaptation (LoRA) of a pretrained LLM with a demand model fitted to available data. At each round, the actor generates an advertisement, and the critic estimates purchase probabilities to guide price selection. Then, the resulting feedback is used to update both the actor and the critic, with the critic's revenue estimates providing a baseline for policy gradient updates of the actor. To evaluate our approach, we develop an evaluation framework with three synthetic demand models and a demand simulator built from real-world marketplace data. Finally, we compare our algorithm with benchmarks that do not jointly optimize price selection and advertisement generation, achieving expected revenue gains over the reference policy of 5.69%, 5.18% and 55.96% under the three synthetic demand models and 5.81% under the marketplace simulator.