Comparison
GSM-IC vs dart-math
Verdict
Pick GSM-IC if a benchmark dataset for assessing the capability of language models in solving arithmetic problems amidst distractions; pick dart-math if dART-Math provides sophisticated difficulty-aware rejection tuning for enhancing mathematical problem-solving capabilities of deep learning models.
Markdown twin · GSM-IC alternatives · dart-math alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | GSM-IC | dart-math |
|---|---|---|
| Maintenance | Archived (1264d since push) As of 3w · github_public_v1 | Dormant (595d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- GSM-IC
- Arithmetic reasoning dataset with irrelevant context to test model distractibility
- dart-math
- Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving
Stars
- GSM-IC
- 67
- dart-math
- 120
Forks
- GSM-IC
- 2
- dart-math
- 8
Open issues
- GSM-IC
- 1
- dart-math
- 5
Language
- GSM-IC
- -
- dart-math
- Jupyter Notebook
Adopt for
- GSM-IC
- A benchmark dataset for assessing the capability of language models in solving arithmetic problems amidst distractions.
- dart-math
- DART-Math provides sophisticated difficulty-aware rejection tuning for enhancing mathematical problem-solving capabilities of deep learning models.
Persona
- GSM-IC
- -
- dart-math
- -
Runtime
- GSM-IC
- -
- dart-math
- -
License
- GSM-IC
- -
- dart-math
- MIT
Last pushed
- GSM-IC
- Feb 13, 2023
- dart-math
- Dec 10, 2024
Categories
- GSM-IC
- Evaluation & Observability
- dart-math
- Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- GSM-IC
- Archived (8%)
- dart-math
- Dormant (18%)
Days since push
- GSM-IC
- 1264d
- dart-math
- 595d
Archived on GitHub
- GSM-IC
- Yes
- dart-math
- No
Open issues (now)
- GSM-IC
- 1
- dart-math
- 5
OSV dependency advisories
- GSM-IC
- No lockfile (source not queried)
- dart-math
- No published findings from this source as of 2026-07-11
Full report
- GSM-IC
- Trust report
- dart-math
- Trust report
Choose GSM-IC if…
- Tags unique to GSM-IC: arithmetic reasoning, gsm8k extension, irrelevant context, model evaluation.
- When evaluating how well a model can ignore irrelevant context to solve grade-school math problems from GSM8K.
- Leaner open-issue backlog (1).
When NOT to use GSM-IC
- If your aim is to evaluate arithmetic reasoning capabilities without any distractions or unnecessary textual information added to the problems.
- When you require a dataset for purely numerical analysis tasks that do not involve understanding language text or filtering out irrelevant information.
Choose dart-math if…
- 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.
- Tags unique to dart-math: deep-learning, llm, llm-evaluation, llm-inference.
- Also covers Inference & Serving, Model Training.
- Consider DART-Math when you need to improve the performance of your model on specific mathematical problems where difficulty is a critical factor.
When NOT to use dart-math
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (google-research-datasets/GSM-IC) · observed Aug 1, 2026
- GitHub forks (google-research-datasets/GSM-IC) · observed Aug 1, 2026
- Last push (google-research-datasets/GSM-IC) · observed Feb 13, 2023
- License file (unknown) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (hkust-nlp/dart-math) · observed Jul 29, 2026
- GitHub forks (hkust-nlp/dart-math) · observed Jul 29, 2026
- Last push (hkust-nlp/dart-math) · observed Dec 10, 2024
- License file (MIT) · observed Jul 29, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: GSM-IC 67 · dart-math 120 (synced Aug 1, 2026).
Common questions
- What is the difference between GSM-IC and dart-math?
- GSM-IC: Arithmetic reasoning dataset with irrelevant context to test model distractibility. dart-math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving. See the comparison table for live GitHub stats and shared categories.
- When should I choose GSM-IC over dart-math?
- Choose GSM-IC over dart-math when Tags unique to GSM-IC: arithmetic reasoning, gsm8k extension, irrelevant context, model evaluation; When evaluating how well a model can ignore irrelevant context to solve grade-school math problems from GSM8K; Leaner open-issue backlog (1).
- When should I choose dart-math over GSM-IC?
- Choose dart-math over GSM-IC when 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; Tags unique to dart-math: deep-learning, llm, llm-evaluation, llm-inference; Also covers Inference & Serving, Model Training; Consider DART-Math when you need to improve the performance of your model on specific mathematical problems where difficulty is a critical factor.
- When should I avoid GSM-IC?
- If your aim is to evaluate arithmetic reasoning capabilities without any distractions or unnecessary textual information added to the problems. When you require a dataset for purely numerical analysis tasks that do not involve understanding language text or filtering out irrelevant information.
- When should I avoid dart-math?
- 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.
- Is GSM-IC or dart-math more popular on GitHub?
- dart-math has more GitHub stars (120 vs 67). Stars measure visibility, not whether either tool fits your constraints.
- Are GSM-IC and dart-math open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to GSM-IC or dart-math?
- GraphCanon lists graph-backed alternatives at GSM-IC alternatives and dart-math alternatives (GSM-IC markdown twin, dart-math markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, GSM-IC or dart-math?
- GSM-IC: Archived. dart-math: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for GSM-IC and dart-math?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: GSM-IC trust report; dart-math trust report.