An RL-trained scientific peer review model based on Qwen3-8B. ProReviewer-8B is fine-tuned using Group Relative Policy Optimization (GRPO) to produce high-quality, evidence-based peer reviews of scientific papers.

Model Description

ProReviewer-8B is the backbone model for the ProReviewer agent, an R1-style reasoning agent that reviews scientific papers through structured investigation rather than passive generation. The model was trained with a multi-stage curriculum:

Training Details

Parameter	Value
Base model	Qwen/Qwen3-8B
Training method	SFT+ GRPO with step-level advantages
Training data	ICLR 2025 papers (UKPLab/ProReviewer-Dataset)
Architecture	Qwen3ForCausalLM
Parameters	8B
Precision	bfloat16

Usage

With vLLM

vllm serve UKPLab/ProReviewer-8B --max-model-len 32768 --dtype bfloat16

With the ProReviewer Agent

The recommended way to use this model is through the ProReviewer agent framework in the ProReviewer:

python

from reviewer.evaluation import run_inference

paper = {
    "paper_id": "example",
    "paper_content": "# Paper Title\n\nAbstract: ...",
    "human_avg_score": 5.0,
}

# Option 1: Use a config name from config.toml (model served via vLLM)
result = await run_inference(paper, model="proreviewer-8B")

# Option 2: Use a local path (loads model directly via vLLM)
result = await run_inference(paper, model="/path/to/ProReviewer-8B")

With Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("UKPLab/ProReviewer-8B", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained("UKPLab/ProReviewer-8B")

Associated Resources

Paper: From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent
Code: UKPLab/arxiv2026-ProReviewer
Dataset: UKPLab/ProReviewer-Dataset

Citation

bibtex

@article{fang2026passive,
  title={From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent},
  author={Fang, Haishuo and Feng, Yue and Gurevych, Iryna},
  journal={arXiv preprint arXiv:2606.13349},
  year={2026}
}

License

This model is released under the MIT License.