---
license: apache-2.0
tags:
- text-generation
- causal-lm
- reasoning
library_name: transformers
---
# Introduction
[](https://arxiv.org/abs/2510.04140)
[](https://github.com/Jiangzs1028/MENTOR)
MENTOR is a framework that enables LLMs to achieve effective and diverse exploration in reinforcement learning by providing expert guidance only at critical decision points, rather than imitating entire expert trajectories.
## Key Highlights
- **Selective Expert Guidance:** Injects expert signals only at critical decision points, avoiding full-trajectory imitation.
- **Effective & Diverse Exploration:** Balances expert guidance with autonomous exploration, preventing entropy collapse.
- **Absorb Essence, Remove Redundancy:** Captures essential expert strategies while discarding unnecessary patterns.
# Chat Template
```python
def build_MENTOR_chat_template(question, tokenizer):
system_prompt = (
"You are a helpful AI Assistant that provides well-reasoned and detailed responses. "
"You FIRST think about the reasoning process as an internal monologue and "
"then provide the final answer. The reasoning process MUST BE enclosed "
"within tags. The final answer MUST BE put in \\boxed{}."
)
return tokenizer.apply_chat_template(
[
{"role": "system", "content": system_prompt},
{"role": "user", "content": question}
],
tokenize=False,
add_generation_prompt=True
)
```
# Citation
If you find our model useful, please kindly cite our paper:
```
@article{jiang2025selective,
title={Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs},
author={Jiang, Zishang and Han, Jinyi and Li, Tingyun and Wang, Xinyi and Jiang, Sihang and Liang, Jiaqing and Dai, Zhaoqian and Ma, Shuguang and Yu, Fei and Xiao, Yanghua},
journal={arXiv preprint arXiv:2510.04140},
year={2025}
}
```