| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: SENSUS Bench v1 (training data and evaluation kit) |
| task_categories: |
| - text-generation |
| tags: |
| - mathematics |
| - education |
| - tutoring |
| - synthetic |
| - supervised-fine-tuning |
| - evaluation |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: training |
| default: true |
| data_files: |
| - split: train |
| path: training/train.jsonl |
| - config_name: evaluation |
| data_files: |
| - split: test |
| path: evaluation/eval.jsonl |
| --- |
| |
|  |
|
|
| # SENSUS Bench v1 |
|
|
| Lorm Labs | 29 October 2026 |
|
|
| SENSUS teaches and tests one tutoring skill: replying to a learner's written |
| reasoning about fractions and decimals (grades 4 and 5). A good reply credits |
| what the learner did correctly, names the likely error tentatively, explains |
| the idea briefly and ends with a question the learner can answer next. |
|
|
| | | | |
| |---|---| |
| | 🎮 **[Play Duel](https://huggingface.co/spaces/Lorm-Labs/sensus-bench-v1)** | Pick the better of two model replies and compare your choices with the graders | |
| | 📄 **[Paper](https://lorm.ai/paper.html)** | Teaching Models to Read and Respond to a Learner's Reasoning | |
| | 🌐 **[lorm.ai](https://lorm.ai)** | Results, the method and every evaluation case | |
|
|
| This repository holds two separate packages: |
|
|
| | Folder | What it is | Use it to | |
| |---|---|---| |
| | [`training/`](training/) | **SENSUS SFT v1.1**: 1,015 synthetic tutoring conversations, the recorded training recipe and step-by-step instructions | fine-tune your own model | |
| | [`evaluation/`](evaluation/) | **SENSUS Bench v1 Evaluation**: the 76 evaluation cases, the scripts that generate and grade replies, and every recorded reply and grade | score your model the same way the published results were scored | |
|
|
| No evaluation question appears in the training data. Keep the two apart: a |
| model trained on the evaluation cases would score higher without having learned |
| the skill. |
|
|
| All conversations and cases are synthetic. They contain no individual student |
| records. |
|
|
| ## Train a model |
|
|
| ```python |
| from datasets import load_dataset |
| training = load_dataset("Lorm-Labs/sensus-bench-v1", "training", split="train") |
| print(len(training)) # 1015 |
| print(training[0]["messages"]) # system, user, assistant |
| ``` |
|
|
| Each row has one field, `messages`: the tutor instruction, the question with |
| what the learner did and said, and the target tutor reply. The full recipe |
| (LoRA rank 32, 8 epochs, learning rate 0.0003) and how to reproduce the |
| recorded runs are in [`training/TRAINING.md`](training/TRAINING.md) and |
| [`training/recipe.json`](training/recipe.json). The dataset card is |
| [`training/README.md`](training/README.md). |
|
|
| ## Evaluate a model |
|
|
| ```python |
| from datasets import load_dataset |
| cases = load_dataset("Lorm-Labs/sensus-bench-v1", "evaluation", split="test") |
| print(len(cases)) # 76 |
| ``` |
|
|
| To score a model, download the `evaluation/` folder and run: |
|
|
| ``` |
| python3 sample.py --model <your-model> --out replies.jsonl |
| python3 grade.py --replies replies.jsonl --out grades.jsonl |
| ``` |
|
|
| `grade.py` uses two model graders and needs your own API keys. The published |
| models' replies and both graders' marks are in `evaluation/recorded/`. Details |
| are in [`evaluation/README.md`](evaluation/README.md). |
|
|
| ## Check the files |
|
|
| Each folder has a `verify.py` that checks every file against its published |
| fingerprints, offline: |
|
|
| ``` |
| cd training && python3 verify.py |
| cd evaluation && python3 verify.py |
| ``` |
|
|
| ## Results and paper |
|
|
| On these 76 cases, Qwen3.8-27B fine-tuned on SENSUS SFT scores 0.825 on average |
| over two training runs, against 0.290 for the same model before training and |
| 0.525 for the highest-scoring general-purpose model tested. The method, results |
| and limits are in the technical report, *Teaching Models to Read and Respond to |
| a Learner's Reasoning*: https://lorm.ai/paper.html |
|
|
| ## License |
|
|
| The data in both folders is released under the Creative Commons Attribution 4.0 |
| International License (CC BY 4.0); see `training/LICENSE` and |
| `evaluation/LICENSE`. The scripts in both folders are released under the MIT License; |
| see `training/LICENSE-CODE` and `evaluation/LICENSE-CODE`. |
|
|
| ## Citation |
|
|
| If you use the benchmark or the results, please cite the paper: |
|
|
| ```bibtex |
| @techreport{wang2026sensus, |
| title = {Teaching Models to Read and Respond to a Learner's Reasoning: SENSUS Bench and an Open SFT Dataset for Grade 4 and 5 Fraction and Decimal Errors}, |
| author = {Wang, Karen}, |
| institution = {Georgetown University}, |
| year = {2026}, |
| month = oct, |
| url = {https://lorm.ai/paper.html} |
| } |
| ``` |
|
|
| If you use the training data, please also cite the dataset: |
|
|
| ```bibtex |
| @misc{lorm2026sensussft, |
| title = {{SENSUS SFT}: Synthetic Tutoring Conversations for Supervised Fine-Tuning}, |
| author = {{Lorm Labs}}, |
| year = {2026}, |
| version = {1.1}, |
| howpublished = {\url{https://lorm.ai/datasets.html}}, |
| note = {Dataset released under CC BY 4.0} |
| } |
| ``` |
|
|
| The evaluation kit's citation is in `evaluation/README.md`. |
|
|
| ## Contact |
|
|
| Use the contact form at https://lorm.ai/contact.html |
|
|
