Marrying Pricing and Advertising with LLMs

summary

The gist

The gist: 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 to jointly optimize sequential pricing

In short

The study proposes an online actor-critic algorithm to jointly optimize sequential pricing and advertisement generation for maximum revenue under unknown product demand. It combines a pre-trained LLM adapted via LoRA for ads (actor) with a demand model (critic) fitted to data. This method significantly improves expected revenue compared to fixed policies.

Key concepts

Sequential Pricing Problem: This is a scenario where a seller must choose both a price and an advertisement before observing the final purchase outcome. The goal is to maximize total revenue across multiple sequential interactions, which is challenging because demand remains unknown.
Actor-Critic Algorithm: The system uses two components: an 'actor' that generates advertisements (using an LLM) and a 'critic' that estimates the expected revenue based on the current price and ad. The actor tries to maximize this estimated revenue, while the critic learns to accurately predict purchase probabilities.
Low-Rank Adaptation (LoRA): LoRA is a technique used to efficiently fine-tune large pre-trained Language Models (LLMs) for generating advertisements. Instead of retraining the entire massive model, LoRA injects small, trainable matrices into the model's layers, allowing it to adapt quickly while preserving the original knowledge.

Terminology used across episodes

Reinforcement Learning (RL) — explained in 25 episodes
Sample Complexity — explained in 19 episodes
Federated Learning — explained in 17 episodes
Retrieval-Augmented Generation (RAG) — explained in 17 episodes
Quantum Fisher Information (QFI) — explained in 16 episodes
Flow Matching — explained in 15 episodes

This episode discusses

The paper

Marrying Pricing and Advertising with LLMs · Read on arXiv

Alessandro Barro Francesco Bacchiocchi, Francesco Emanuele Stradi Alberto Marchesi

Politecnico di Milano

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Marrying Pricing and Advertising with LLMs".

Jane: The gist:

Tom: First, who's behind it and why it matters.

Paper summary: Tom: So we've seen how this online actor-critic algorithm works, combining LoRA on an LLM for ads and a demand model to guide price selection based on purchase feedback > Page four ---

Jane: And the key finding is that this joint optimization framework leads to measurable increases in expected revenue compared to just optimizing price or just optimizing ad content separately.

Lu: The paper shows that you can marry the two decisions—price and advertisement—in a sequential setting where demand is unknown, using LLMs as the mechanism for generating those ads > Page two ---

Meng: From my side, what I see is that it's a solid framework for testing how AI can handle multi-objective optimization when there are complex, coupled feedback loops > Page three ---

Tom: Exactly. The implication is that if you have data to fit a demand model and an LLM that can generate relevant content, you can jointly optimize revenue in these types of dynamic scenarios.

Jane: It moves the conversation from just one side of the coin—like just setting a price—to understanding how creative choices, like an ad, fundamentally change the pricing landscape > Page one --- <ref:2610.09985#pg1>

Lu: The paper does suggest that future work could look at making this system robust when demand shifts dramatically over time, as tracking that moving target is hard > Page four ---

Meng: And from a deployment angle, they'll need to prove that the online adaptation process doesn't introduce instability when the marketplace gets very busy and data streams become unpredictable > Page four ---

Tom: That’s the practical stuff. It’s not just about getting a high number on a test set; it’s about making sure this system stays stable when you deploy it live > Page one --- <ref:2610.09985#pg1>

Conclusion: Tom: So we’re wrapping up our look at "Marrying Pricing and Advertising with LLMs," this paper is about how you can use a large language model to handle both setting a price and picking an ad at the same time when you don't know what customers will do.

Jane: Right, so the main idea is taking something that generates ads, like an LLM, and figuring out how to pair it with pricing in this online selling thing, where you only get feedback after someone buys or not.

Lu: The authors are looking at sequential posted pricing problems where the seller has to pick a price and an ad based on some product context before seeing if the buyer actually takes the deal.

Meng: From what I see, it’s trying to solve that tricky joint optimization problem where you have two decisions—price and creative content—that both affect revenue in a dynamic setting.

Tom: Exactly, they use an online actor-critic method where one part of the system is this LLM fine-tuned with low-rank adaptation just for making those ads.

Jane: And the other part, the critic, that’s a demand model fitted to whatever data you have to estimate the purchase probability given a price and an ad.

Lu: The methodology involves the actor generating an advertisement in batches, and then the seller choosing a price guided by what that critic predicts will bring in revenue.

Meng: What I’m thinking is how they handle that uncertainty—the exploration part where you sometimes pick a random price just to see what happens—and how they balance getting better ads with finding the right price.

Tom: They use this leave-one-out baseline for their actor updates, which is interesting because it means the gradient update doesn't rely on an exact guess of the reward, which reduces some of that noise from individual purchase outcomes.

Jane: So what does this actually change for someone who just listens to the show? It suggests that a model can learn to create a better ad and set a better price simultaneously, instead of treating them as separate problems.

Lu: The results show these combined policies actually increase expected revenue across different demand scenarios, like synthetic ones and even some real-world marketplace data.

Meng: But the caveat is that it’s still an online process, so we have to worry about how stable this whole system is when the market feedback keeps changing rapidly.

Tom: It’s a solid framework for testing how you can couple generation with optimization in these kinds of sequential decision-making tasks.

Jane: And the paper points toward making this even more interesting by looking at what happens if demand itself starts to change over time, which is a big challenge for any system like this.

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