---
base_model:
- Qwen/Qwen2.5-7B-Instruct
datasets:
- virtuoussy/Multi-subject-RLVR
- sarosavo/Master-RM
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: apache-2.0
pipeline_tag: text-classification
---

# Robust Reward Model for LLM-as-a-Judge

This repository contains a robust, general-domain generative reward model presented in the paper [One Token to Fool LLM-as-a-Judge](https://huggingface.co/papers/2507.08794).

- **Paper**: [One Token to Fool LLM-as-a-Judge](https://huggingface.co/papers/2507.08794)
- **Training Data**: [https://huggingface.co/datasets/sarosavo/Master-RM](https://huggingface.co/datasets/sarosavo/Master-RM)
<!-- - **Code/GitHub Repository**: [https://github.com/Yulai-Zhao/Robust-Reward-Model](https://github.com/Yulai-Zhao/Robust-Reward-Model) -->
- **Training algorithm**: Standard supervised fine-tuning, see Appendix A.2 for more details.

## Model Description

Generative reward models (also known as LLMs-as-judges), which use large language models (LLMs) to evaluate answer quality, are increasingly adopted in reinforcement learning with verifiable rewards (RLVR). They are often preferred over rigid rule-based metrics, especially for complex reasoning tasks involving free-form outputs. Despite the seeming simplicity of this comparison task, existing generative reward models exhibit surprising vulnerabilities to superficial manipulations: non-word symbols (e.g., ":" or ".") or reasoning openers like "Thought process:" and "Let's solve this problem step by step." can often lead to false positive rewards.

We find that such weakness is widespread across various LLMs, datasets, and prompt formats, posing a serious threat to core algorithmic paradigms relying on generative reward models, such as rejection sampling, preference optimization, and RLVR. 

To mitigate this issue, we train a robust general-domain generative model by leverating a simple yet effective data augmentation strategy. Our reward model demonstates substantially improved robustness over the most advanced commencial models (e.g., GPT-4o, GPT-o1, Claude-4) and specialized generative verifiers (e.g., Omni-Judge, Generative-Verifier).

## How to use

Inputting the question, its ground-truth reference, and the response to be evaluated, the model will judge its correctness. An example inference script is provided below.

> ```python
> from transformers import AutoTokenizer, AutoModelForCausalLM
> 
> tokenizer = AutoTokenizer.from_pretrained("sarosavo/Master-RM")
> model = AutoModelForCausalLM.from_pretrained("sarosavo/Master-RM")
> 
> PROMPT= '''
> Given a problem, determine whether the final answer in the provided (incomplete) solution process matches the reference answer.  
> The reference answer may be one single option character (e.g., A, B, C, D), a numerical value, an expression, or a list of answers if multiple questions are involved.  
> **The reference answer may be in Chinese or another language, but your evaluation should be language-agnostic.**  
> 
> Your task:  
> - Compare the final output of the solution process with the reference answer.  
> - If they **match exactly**, output **YES**.  
> - If they **do not match**, output **NO**.  
> - If the solution process is unclear, incomplete, or ambiguous, assume it is incorrect and output **NO**.  
> 
> Your output must be strictly **'YES'** or **'NO'**, with no additional words, punctuation, or explanation.  
> 
> ---
> 
> **Question:**  
> {question}  
> 
> **Solution Process (Final Step Only):**  
> {response}  
> 
> **Reference Answer:**  
> {reference}  
> 
> **Output:**  
> '''
> 
> 
> question="The founder of China's first public kindergarten teacher training school - Jiangxi Experimental Kindergarten Teacher School is (　　)."
> label="Chen Heqin"
> answer="heqin chen"
> 
> prompt_question = PROMPT.format(question=question, reference=label, response=answer)
> messages=[
>            {"role": "system", "content": "You are a helpful assistant."},
>            {"role": "user", "content": prompt_question},
>          ]
> 
> input_ids=tokenizer.apply_chat_template(messages,return_tensors="pt")
> output=model.generate(input_ids,do_sample=False)
> judgement=tokenizer.decode(output[0][input_ids.shape[1]:],skip_special_tokens=True)
> print("Model judgement: ",judgement)
> ```

## Use this reward model for RLVR training

### 1. Launch a remote reward server with vllm

The script below will launch a reward at http://127.0.0.1:8000/get_reward

```bash
bash reward_server/launch_reward.sh {MODEL_PATH} {ANSWER_PATH} {METRIC}

# MODEL_PATH: the path of our reward model.
# ANSWER_PATH: the path of the training data.
# METRIC: greedy/prob
# This will launch a reward at http://127.0.0.1:8000/get_reward
```

### 2. Start RLVR training

```bash
bash reward_server/RLVR_train.sh {METHOD} {PRETRAIN_PATH} {DATA_PATH} {REWARD_API}

# METHOD:          advantage estimator, e.g., reinforce_baseline, reinforce, rloo
# PRETRAIN_PATH:   path to the pretrained model, e.g., Qwen2.5-7B
# DATA_PATH:       path to the QA data with which we want to perform RL reasoning
# REWARD_API:      remote reward server url, e.g., http://127.0.0.1:8000/get_reward
```

## Citation

If you use this model, please cite:

```bibtex
@article{zhao2025one,
  title={One Token to Fool LLM-as-a-Judge},
  author={Zhao, Yulai and Liu, Haolin and Yu, Dian and Kung, S.Y. and Mi, Haitao and Yu, Dong},
  journal={arXiv preprint arXiv:2507.08794},
  year={2025}
}
```

## Acknowledgements

The development of this model is built upon [Qwen2.5-7B-Instruct-RLVR](https://huggingface.co/virtuoussy/Qwen2.5-7B-Instruct-RLVR).