SWE-Review-8B/README.md

---
license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
  - code-review
  - software-engineering
  - swe-bench
  - agentic
pipeline_tag: text-generation
---

# SWE-Review-8B

<p align="center">
<img src="assets/pipeline_overview.png" width="100%"/>
</p>


An agentic code review model fine-tuned from [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on **8,914 review trajectories** ([SWE-Review-Traj](https://huggingface.co/datasets/SWE-Lego/SWE-Review-Traj)). The model explores a repository via tool calls, independently traces the root cause of an issue, and produces a structured review decision with diagnostic feedback for revision.

[**Project Page**](https://swe-lego.github.io/SWE-Review/) | [**Paper**](https://arxiv.org/abs/2607.06065) | [**Code**](https://github.com/SWE-Lego/SWE-Review) | [**Benchmark**](https://huggingface.co/datasets/SWE-Lego/SWE-Review-Bench) | [**Training Data**](https://huggingface.co/datasets/SWE-Lego/SWE-Review-Traj) | [**Claude Code Plugin**](https://github.com/SWE-Lego/cc-swe-review)

## About SWE-Review

**SWE-Review** is a framework for closing the issue-resolution loop with agentic code review.
A reviewer agent independently explores the repository, traces the root cause, and compares its own diagnosis against the submitted PR — turning one-shot patch generation into an iterative generate-review-revise loop that raises resolve rates by up to +29.4 percentage points on SWE-bench Verified.

## Quick Start

### Serve with vLLM

```bash
python -m vllm.entrypoints.openai.api_server \
    --model SWE-Lego/SWE-Review-8B \
    --served-model-name SWE-Review-8B \
    --host 0.0.0.0 --port 8000 \
    --tensor-parallel-size 4 \
    --gpu-memory-utilization 0.9 \
    --max-model-len 131072 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --chat-template-content-format string \
    --api-key dummy-key
```

### Run a Review

The model is designed to be used as an agentic reviewer with tool-calling (file reading, code search, etc.). See the [SWE-Review code repository](https://github.com/SWE-Lego/SWE-Review) for the full agentic review pipeline, including Harbor-based orchestration and benchmark evaluation scripts.

For a plug-and-play experience in Claude Code, install the [cc-swe-review plugin](https://github.com/SWE-Lego/cc-swe-review).

## Evaluation Results

Performance on [SWE-Review-Bench](https://huggingface.co/datasets/SWE-Lego/SWE-Review-Bench) (1,384 instances across 3 quality tiers):

| Split | CR (%) | DA (%) | RRR (%) | Δ RR |
|-------|--------|--------|---------|------|
| GLM-5 (high, baseline 72.2%) | 84.2 | 68.7 | 71.6 | -0.6 |
| Coder-30B (medium, baseline 50.9%) | 81.4 | 66.9 | 52.8 | +1.9 |
| Qwen3-30B (low, baseline 27.5%) | 71.1 | 71.6 | 35.1 | +7.6 |

### Improvement over Base Model (Qwen3-8B without SFT)

| Split | DA (base → SFT) | RRR (base → SFT) |
|-------|-----------------|-------------------|
| GLM-5 (high) | 49.0 → 68.7 (+19.7) | 72.2 → 71.6 (-0.6) |
| Coder-30B (medium) | 49.1 → 66.9 (+17.8) | 50.9 → 52.8 (+1.9) |
| Qwen3-30B (low) | 50.8 → 71.6 (+20.8) | 27.5 → 35.1 (+7.6) |

SFT dramatically improves DA across all splits. On harder PRs (medium/low tiers), review-guided revision further raises resolve rate.

### Test-Time Scaling

When used as the reviewer in iterative review-revision loops, the model enables **3.7× the test-time scaling gain at 6.7× the efficiency** of independent resampling, reaching 38.4% resolve rate with only 2.44 samples on average.

## Training Details

| Property | Value |
|----------|-------|
| Base Model | [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
| Training Data | [SWE-Review-Traj](https://huggingface.co/datasets/SWE-Lego/SWE-Review-Traj) (8,914 trajectories) |
| Framework | [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) + DeepSpeed ZeRO-3 |
| Epochs | 4 |
| Learning Rate | 1e-4 (cosine scheduler) |
| Max Context Length | 131,072 tokens (YaRN rope scaling) |
| Optimizations | Liger kernel + Flash Attention v2 |

## Important Notes

- Use `--tool-call-parser hermes` (not `qwen3_coder`)
- Set `OPENHANDS_LLM_NATIVE_TOOL_CALLING=true` when using with OpenHands
- Do **not** use `--reasoning-parser` with this model
- For Harbor agent execution, use `http://172.17.0.1:<port>/v1` (Docker bridge gateway), not `localhost`

## Citation

```bibtex
@misc{wang2026swereview,
  title={SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review},
  author={Ruoyu Wang and Jierun Chen and Shaowei Wang and Chaofan Tao and Sidi Yang and Yuxin Jiang and Kim-Hui Yap and Lifeng Shang and Xiaohui Li and Haoli Bai},
  year={2026},
  eprint={2607.06065},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/2607.06065}
}
```