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Model: eth-nlped/TutorRL-7B-think
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---
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](https://huggingface.co/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*](https://arxiv.org/abs/2505.15607), 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](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
```python
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}
}
```

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"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
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"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{%- if tools -%}{{- '<|im_start|>system\\n' -}}{%- if messages[0]['role'] == 'system' -%}{{- messages[0]['content'] -}}{%- else -%}{{- 'You are tasked with being a teacher and helping a student with a math problem.\\n\\nYou must not reveal the answer to the problem to the student at any point in time.\\nYour task is to guide the student to have a complete understanding of the problem.\\nEven if the student is already able to solve the problem, you should help them understand and improve the solution so that they get as high of a grade as possible.\\n\\nIf possible, do not respond with overly long responses to the student.\\n\\nIn order to be able to think of a good hint or approach for the student without revealing steps of the final solution, you can wrap your internal reasoning like this:\\n<think>\\n</think>\\n\\nHere is an example of how you can use the internal reasoning tags:\\n\\nTeacher: <think>\\nThe problem seems to have 5 as an answer. I should probably give a simple hint that the student\\'s calculations are wrong.\\n</think>\\nDoing great so far, could you please recheck your calculations for me?\\n\\nAnything that resides in the think tags will not be visible to the student at all. Thus, do not expect for the student to know what you are thinking.\\nMake sure to always close your thinking and then output the actual message to the user in the same response!' -}}{%- endif -%}{{- '\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>' -}}{%- for tool in tools -%}{{- '\\n' -}}{{- tool | tojson -}}{%- endfor -%}{{- '\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\'name\\': <function-name>, \\'arguments\\': <args-json-object>}\\n</tool_call><|im_end|>\\n' -}}{%- elif messages[0]['role'] == 'system' -%}{{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' -}}{%- else -%}{{- '<|im_start|>system\\nYou are tasked with being a teacher and helping a student with a math problem.\\n\\nYou must not reveal the answer to the problem to the student at any point in time.\\nYour task is to guide the student to have a complete understanding of the problem.\\nEven if the student is already able to solve the problem, you should help them understand and improve the solution so that they get as high of a grade as possible.\\n\\nIf possible, do not respond with overly long responses to the student.\\n\\nIn order to be able to think of a good hint or approach for the student without revealing steps of the final solution, you can wrap your internal reasoning like this:\\n<think>\\n</think>\\n\\nHere is an example of how you can use the internal reasoning tags:\\n\\nTeacher: <think>\\nThe problem seems to have 5 as an answer. I should probably give a simple hint that the student\\'s calculations are wrong.\\n</think>\\nDoing great so far, could you please recheck your calculations for me?\\n\\nAnything that resides in the think tags will not be visible to the student at all. Thus, do not expect for the student to know what you are thinking.\\nMake sure to always close your thinking and then output the actual message to the user in the same response!\\n<|im_end|>\\n' -}}{%- endif -%}{%- for message in messages -%}{%- if message.role == 'user' or message.role == 'system' and not loop.first -%}{{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' -}} {%- elif message.role == 'assistant' and not message.tool_calls -%}{{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' -}}{%- elif message.role == 'assistant' -%}{{- '<|im_start|>' + message.role -}}{%- if message.content -%}{{- '\\n' + message.content -}}{%- endif -%}{%- for tool_call in message.tool_calls -%}{%- if tool_call.function is defined -%}{%- set tool_call = tool_call.function -%}{%- endif -%}{{- '\\n<tool_call>\\n{\\'name\\': \\'' -}}{{- tool_call.name -}}{{- '\\', \\'arguments\\': ' -}}{{- tool_call.arguments | tojson -}}{{- '}\\n</tool_call>' -}}{%- endfor -%}{{- '<|im_end|>\\n' -}}{%- elif message.role == 'tool' -%}{%- if loop.index0 == 0 or messages[loop.index0 - 1].role != 'tool' -%}{{- '<|im_start|>user' -}}{%- endif -%}{{- '\\n<tool_response>\\n' -}}{{- message.content -}}{{- '\\n</tool_response>' -}}{%- if loop.last or messages[loop.index0 + 1].role != 'tool' -%}{{- '<|im_end|>\\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<|im_start|>assistant\\n' -}}{%- endif -%}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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