Datasets:

Lorm-Labs
/

sensus-bench-v1

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	Pick the better of two model replies and compare your choices with the graders
📄 Paper	Teaching Models to Read and Respond to a Learner's Reasoning
🌐 lorm.ai	Results, the method and every evaluation case

This repository holds two separate packages:

Folder	What it is	Use it to
`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/`	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

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 and training/recipe.json. The dataset card is training/README.md.

Evaluate a model

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.

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:

@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:

@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