library_name: transformers
license: apache-2.0
license_link: https://github.com/eth-lre/PedagogicalRL/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-7B-Instruct
tags:
- math-tutor
- grpo
datasets:
- SynthLabsAI/Big-Math-RL-Verified
TutorRL-7B-think
Overview
TutorRL-7B-think is a fine-tuned variant of Qwen/Qwen2.5-7B-Instruct, trained to act as a math tutor rather than a solver. It is aligned to pedagogical principles using reinforcement learning (GRPO) in a synthetic multi-turn classroom setting, without requiring any human-labeled data.
This model was developed as part of the research project From Problem-Solving to Teaching Problem-Solving, which proposes a scalable, annotation-free approach to training LLMs as educational tutors. Instead of directly answering questions, the model is optimized to scaffold reasoning, guide through Socratic questioning, and withhold final solutions when beneficial for learning.
Repository: https://github.com/eth-lre/PedagogicalRL
Intended Use
This model is intended for use in:
- Interactive math tutoring
- Socratic dialogue generation
- Research on educational alignment of LLMs
- Safe and indirect teaching in problem-solving contexts
Thinking
This model variant allows for hidden thinking.
The thinking content is enclosed in tags: <think> ... </think>.
Example Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "eth-nlped/TutorRL-7B-think"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{"role": "user", "content": "Can you help me solve 3x + 5 = 20?"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Citation
If you use this model or build upon the training framework, please cite:
@misc{dinucujianu2025problemsolvingteachingproblemsolvingaligning,
title={From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning},
author={David Dinucu-Jianu and Jakub Macina and Nico Daheim and Ido Hakimi and Iryna Gurevych and Mrinmaya Sachan},
year={2025},
eprint={2505.15607},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.15607}
}