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Decision brief
DART-Math provides sophisticated difficulty-aware rejection tuning for enhancing mathematical problem-solving capabilities of deep learning models.
Good fit when
- Consider DART-Math when you need to improve the performance of your model on specific mathematical problems where difficulty is a critical factor.
- Use DART-Math if your dataset includes a wide range of complexity levels in problem solving, as it can adapt better by using the Prop2Diff methodology.
Avoid when
- Avoid using DART-Math when simplicity and ease-of-implementation are prioritized over performance gains on complex mathematical problems.
- Do not use DART-Math if your application does not require fine-tuning for varying levels of difficulty in problem-solving scenarios; simpler methods may suffice.
- Requirements:
- Min 8 GB RAM; Requires a solid understanding of deep learning frameworks like TensorFlow or PyTorch; Primarily developed for Python environment with packages such as Jupyter Notebook
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (649d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 20, 2026
- Security (OSV)
- No criticals
- As of Jul 11, 2026
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Install
git clone https://github.com/hkust-nlp/dart-mathSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides code and resources for DART-Math, a method that uses difficulty-aware rejection tuning to improve mathematical problem-solving in deep learning models.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 20, 2026)
v-file#tool-integrated-reasoning-reasoning-in-natural-language-interleaved-with-python-code).Source link
Tags
README
🎯DART Math Difficulty Aware Rejection Tuning for Mathematical Problem Solving \[NeurIPS 2024\] Yuxuan Tong, Xiwen Zhang, Rui Wang, Ruidong Wu, Junxian He 📝 Paper@arXiv \ 🤗 Datasets&Models@HF \ 🐱 Code@GitHub \ 💡 Slides \ 🏆 Published@NeurIPS 2024 🐦 Thread@X(Twitter) \ 🐶 中文博...
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