license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model: qwen/Qwen3-8B
tags:
- academic-rebuttal
- agentic-framework
- rl
DRPG Judge Model
This repository contains the Judge Model for the DRPG (Decompose, Retrieve, Plan, Generate) framework, as introduced in the paper DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal.
The model is specifically designed to evaluate the quality of academic rebuttals. It was trained from Qwen3-8B using Reinforcement Learning (RL) to provide accurate and persuasive assessment scores.
Links
- Paper: DRPG: An Agentic Framework for Academic Rebuttal
- Repository: ulab-uiuc/DRPG-RebuttalAgent
About DRPG
DRPG is an agentic framework for automatic academic rebuttal generation that operates through four steps:
- Decompose: Breaking reviews into atomic concerns.
- Retrieve: Finding relevant evidence from the paper.
- Plan: Identifying feasible rebuttal strategies.
- Generate: Creating targeted responses.
The Judge Model is used within this pipeline to assess rebuttal quality, achieving performance beyond the average human level in experimental evaluations.
Usage
Refer to the official GitHub repository for instructions on running the evaluation scripts and using the model within the DRPG pipeline.
Citation
If you find this model useful in your research, please cite:
@article{han2025drpg,
title={DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal},
author={Han, Peixuan and Yu, Yingjie and Xu, Jingjun and You, Jiaxuan},
journal={arXiv preprint arXiv:2601.18081},
url={https://arxiv.org/pdf/2601.18081},
year={2026}
}