Rushes: A Human Preference Dataset for Pluralistic Alignment
We introduce Rushes, a dataset and benchmark for studying revealed human engagement preferences in interactive narrative environments. Rushes is collected through a game interface where users interact with AI-generated branching narratives and select one choice from a small, explicit candidate set at each decision point. Each interaction logs the full candidate set, the user's choice, and the evolving narrative context, yielding time-ordered trajectories with persistent user-level identifiers. Rushes contains 44,226 decision events from 8,167 unique users across six games, capturing sequential, personalized engagement behavior rather than static judgments. We show that user choices exhibi...
| Authors | Michael Xu, Jorge Leandro, Sudha Rao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
Models and datasets referencing this paper
- microsoft/rushesDataset
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