SalesSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators

TL;DR

SalesSim is a new framework for evaluating multimodal language models' ability to simulate realistic, persona-driven retail conversations, highlighting current limitations and proposing reinforcement learning improvements.

Contribution

The paper introduces SalesSim, a benchmark and testbed for assessing and improving multimodal language models' fidelity in retail user simulation, emphasizing decision alignment and conversational quality.

Findings

Models produce fluent conversations but lack lexical diversity.

Models tend to be persuaded by suggestions and drift from personas.

UserGRPO improves decision alignment by 13.8% and conversational quality.

Abstract

We present SalesSim, a framework and testbed for evaluating the ability of Multimodal Large Language Models (MLLMs) to simulate realistic, persona-driven customer behavior in multi-turn, multi-modal, tool-augmented online retail conversations. Unlike prior work that treat user simulation as surface-level dialogue generation, SalesSim models retail interaction and decision-making as a grounded, agentic process, where shoppers with diverse backgrounds, preferences, and dealbreakers interact with a sales agent, seek clarifications, and make informed purchasing decisions. For evaluation, we design a suite of metrics centered on decision alignment, measuring the consistency between the simulator's actions and its persona specifications, as well as conversational quality. We find several behavioral gaps after benchmarking 6 open and closed-source state-of-the-art models. First, while models produce fluent conversations, they display significantly lower lexical diversity and overdisclosure of criteria across personas compared to human conversations. Second, models tend to be persuaded by sales agent suggestions and drift from persona specifications. Even the strongest model achieves less than 79% average alignment with its underlying persona specifications. To make progress on these limitations, we propose UserGRPO, a multi-turn, multi-objective reinforcement learning recipe to optimize both conversational fluency and decision alignment under persona specifications. Our experiments demonstrate that UserGRPO boosts decision alignment of the baseline model by 13.8% while improving conversational quality. By introducing SalesSim, we provide a new testbed for the community to investigate and improve the adherence of user simulators in goal-oriented settings.