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YongqiLi/R-EMID-Estimator
R-EMID-Estimator is a machine learning model from YongqiLi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-sa-4.0.
[](https://arxiv.org/abs/2512.17270) [](https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/RPM-Generalization) [](https://huggingface.co/datasets/YongqiLi/RPGBench)
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Updated Apr 7, 2026
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From the Hugging Face model README
This is a LoRA adapter for computing the R-EMID (Reasoning-based Effective Mutual Information Difference) metric, introduced in the ACL 2026 Findings paper "Understanding Generalization in Role-Playing Models via Information Theory".
The R-EMID Estimator is trained via CoRL (Co-Evolving Reinforcement Learning), the method proposed in the paper. It is used to estimate the R-EMID metric, which measures the generalization performance of role-playing models (RPMs) from an information-theoretic perspective.
This model is intended for evaluation and metric computation rather than direct role-playing.
@inproceedings{li-2026-RPMG,
title = {Understanding Generalization in Role-Playing Models via Information Theory},
author = {Yongqi Li and Hao Lang and Fei Huang and Tieyun Qian and Yongbin Li},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
year = {2026}
}