README.md

---
license: apache-2.0
---

# CompassJudger-2

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<span style="color:red ">TODO</span>.

## Introduction

We introduce **CompassJudger-2**, a novel series of generalist judge models designed to overcome the narrow specialization and limited robustness of existing LLM-as-judge solutions. Current judge models often struggle with comprehensive evaluation, but CompassJudger-2 addresses these limitations with a powerful new training paradigm.

Key contributions of our work include:
- **Advanced Data Strategy:** We employ a task-driven, multi-domain data curation and synthesis strategy to enhance the model's robustness and domain adaptability.
- **Verifiable Reward-Guided Training:** We supervise judgment tasks with verifiable rewards, guiding the model's intrinsic reasoning through chain-of-thought (CoT) and rejection sampling. A refined margin policy gradient loss further enhances performance.
- **Superior Performance:** CompassJudger-2 achieves state-of-the-art results across multiple judge and reward benchmarks. Our 7B model demonstrates competitive accuracy with models that are significantly larger.
- **JudgerBenchV2:** We introduce a new, comprehensive benchmark with 10,000 questions across 10 scenarios, using a Mixture-of-Judgers (MoJ) consensus for more reliable ground truth.

This repository contains the **CompassJudger-2** series of models, fine-tuned on the Qwen2.5-Instruct series.

## Model Downloads
| Model Name | Size | Base Model | Download | Notes |
|:---|:---:|:---|:---:|:---|
| 👉 **CompassJudger-2-7B-Instruct** | 7B | Qwen2.5-7B-Instruct | 🤗 [Model](https://huggingface.co/opencompass/CompassJudger-2-7B-Instruct) | Fine-tuned for generalist judge capabilities. |
| 👉 **CompassJudger-2-32B-Instruct** | 32B | Qwen2.5-32B-Instruct| 🤗 [Model](https://huggingface.co/opencompass/CompassJudger-2-32B-Instruct) | A larger, more powerful judge model. |

## Requirements
You will need to install the latest versions of `transformers` and `accelerate`:

```bash
pip install -U transformers accelerate torch
```

## Quickstart

Here is a simple example demonstrating how to load the model and use it for pairwise evaluation.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "opencompass/CompassJudger-2-7B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """your prompt"""

messages = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=2048
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

```

## Evaluation

CompassJudger-2 sets a new state-of-the-art for judge models, outperforming general models, reward models, and other specialized judge models across a wide range of benchmarks.

| Model | JudgerBench V2 | JudgeBench | RMB | RewardBench | Average |
|:---|:---:|:---:|:---:|:---:|:---:|
| **7B Judge Models** |
| CompassJudger-1-7B-Instruct | 57.96 | 46.00 | 38.18 | 80.74 | 55.72 |
| Con-J-7B-Instruct | 52.35 | 38.06 | 71.50 | 87.10 | 62.25 |
| RISE-Judge-Qwen2.5-7B | 46.12 | 40.48 | 72.64 | 88.20 | 61.61 |
| **CompassJudger-2-7B-Instruct** | **60.52** | **63.06** | **73.90** | **90.96** | **72.11** |
| **32B+ Judge Models** |
| CompassJudger-1-32B-Instruct | 60.33 | 62.29 | 77.63 | 86.17 | 71.61 |
| Skywork-Critic-Llama-3.1-70B | 52.41 | 50.65 | 65.50 | 93.30 | 65.47 |
| RISE-Judge-Qwen2.5-32B | 56.42 | 63.87 | 73.70 | 92.70 | 71.67 |
| **CompassJudger-2-32B-Instruct** | **62.21** | **65.48** | 72.98 | **92.62** | **73.32** |
| **General Models (for reference)** |
| Qwen2.5-32B-Instruct | 62.97 | 59.84 | 74.99 | 85.61 | 70.85 |
| DeepSeek-V3-0324 | 64.43 | 59.68 | 78.16 | 85.17 | 71.86 |
| Qwen3-235B-A22B | 61.40 | 65.97 | 75.59 | 84.68 | 71.91 |


For detailed benchmark performance and methodology, please refer to our [📑 Paper](https://github.com/open-compass/CompassJudger). <span style="color:red ">TODO</span>.

## License

This project is licensed under the Apache 2.0 License. See the [LICENSE](https://github.com/open-compass/CompassJudger/blob/main/LICENSE) file for details. <span style="color:red ">TODO</span>.

## Citation

If you find our work helpful, please consider citing our paper:

<span style="color:red ">TODO</span>.