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Model: UKPLab/ProReviewer-8B Source: Original Platform
2026-07-29 06:15:25 +08:00
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+---
+library_name: transformers
+base_model: Qwen/Qwen3-8B
+model_type: qwen3
+pipeline_tag: text-generation
+license: mit
+language:
+  - en
+tags:
+  - peer-review
+  - scientific-papers
+  - GRPO
+  - reinforcement-learning
+  - paper-review
+datasets:
+  - UKPLab/ProReviewer-Dataset
+citation: |
+  @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}
+  }
+---
+
+# ProReviewer-8B
+
+An RL-trained scientific peer review model based on [Qwen3-8B](https://huggingface.co/Qwen/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](https://huggingface.co/datasets/UKPLab/ProReviewer-Dataset)) |
+| Architecture | Qwen3ForCausalLM |
+| Parameters | 8B |
+| Precision | bfloat16 |
+
+## Usage
+
+### With vLLM
+
+```bash
+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](https://github.com/UKPLab/arxiv2026-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](https://arxiv.org/abs/2606.13349)
+- **Code**: [UKPLab/arxiv2026-ProReviewer](https://github.com/UKPLab/arxiv2026-ProReviewer)
+- **Dataset**: [UKPLab/ProReviewer-Dataset](https://huggingface.co/datasets/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](https://opensource.org/licenses/MIT).