Datasets:

FlaneAI
/

GPT-6.1-Sol-Problem-solving-Reasoning-Distilled

---
pretty_name: "Logical Reasoning & Problem Solving — Filtered SFT Dataset"
license: gpl-2.0
language:
  - en
task_categories:
  - text-generation
tags:
  - reasoning
  - logical-reasoning
  - logic
  - problem-solving
  - supervised-fine-tuning
  - sft
  - instruction-tuning
  - instruction-following
  - llm
  - language-model-training
  - synthetic-data
  - filtered-data
  - reasoning-dataset
  - multi-step-reasoning
  - analytical-reasoning
  - text
  - datasets
  - gpt-6.1-sol
annotations_creators:
  - machine-generated
---
# Logical Reasoning & Problem Solving — Filtered SFT Dataset

**A filtered, synthetic dataset for supervised fine-tuning of language models on logical reasoning and problem-solving tasks.**

This dataset is intended for developers and researchers training models to interpret problems, reason through constraints, and produce coherent solutions. The examples were generated using **GPT-6.1 Sol** and subsequently filtered before release.

Rather than positioning the collection as a general-purpose instruction dataset, it focuses on a specific training objective: **improving how language models approach and answer problems that require logical reasoning**.

## At a glance

| Property | Details |
| --- | --- |
| Primary use | Supervised fine-tuning (SFT) |
| Focus | Logical reasoning and problem solving |
| Data origin | Synthetic examples generated with GPT-6.1 Sol |
| Processing | Post-generation filtering |
| Modality | Text |
| License | GNU General Public License v2.0 (GPL-2.0) |

## Why use this dataset?

Reasoning-oriented fine-tuning benefits from training examples that stay focused on the task. This collection provides a filtered source of synthetic problem-solving data for experiments where reasoning behavior matters more than broad conversational coverage.

It can be useful for:

- **Reasoning-focused SFT:** Adapt instruction-following models to tasks requiring analysis and logical consistency.
- **Problem-solving instruction tuning:** Include problem-and-solution examples in a broader fine-tuning mixture.
- **Training-data research:** Investigate how filtered synthetic data affects model behavior.
- **Targeted fine-tuning experiments:** Compare general instruction tuning with reasoning-focused training mixtures.

The dataset can be used as a standalone training source or combined with other datasets, depending on the target model and training objective.

## Data creation and filtering

The examples were **generated with GPT-6.1 Sol** and **filtered after generation** to produce the published collection.

Filtering is part of the dataset preparation process, but it should not be interpreted as a guarantee that every example is correct, unique, or free of ambiguity. Synthetic reasoning data can contain plausible yet incorrect solutions, incomplete assumptions, and repetitive patterns. Users working on accuracy-sensitive tasks should validate representative samples before training.

No benchmark gains or independent human-verification results are claimed here.

## Getting started

Load the dataset through the Hugging Face `datasets` library:

```python
from datasets import load_dataset
dataset = load_dataset("FlaneAI/GPT-6.1-Sol-Problem-solving-Reasoning-Distilled")
print(dataset)
print(dataset[list(dataset.keys())[0]][0])
```


For best results, review sample quality, apply the prompt formatting required by your model, and evaluate on an independent reasoning benchmark that is not included in training.

## Recommended usage

For a reliable fine-tuning workflow:

1. Inspect the dataset schema and a representative sample of entries.
2. Check that the problem statements and target responses match your intended training objective.
3. Use a consistent chat or instruction template for your base model.
4. Keep evaluation data separate from the training corpus and check for overlap with known benchmarks.
5. Measure both task accuracy and general instruction-following quality after fine-tuning.

## Limitations

This dataset is **synthetically generated**, not a collection of independently verified formal proofs. Even after filtering, individual examples may contain reasoning errors, misleading assumptions, or answers that sound convincing without being correct.

Performance improvements are not guaranteed. Outcomes will depend on the base model, training setup, data mixture, and evaluation methodology. The dataset should not be treated as a standalone benchmark or as ground truth for safety-critical decisions.

## License

The dataset is published under the **GNU General Public License, version 2.0 (GPL-2.0)**. Review the license terms before redistribution or reuse. Users are also responsible for checking any applicable rights and terms associated with the underlying generated content and their intended use.

License reference: https://www.gnu.org/licenses/old-licenses/gpl-2.0.html

## Feedback and contributions

Issues, error reports, and practical feedback from fine-tuning experiments are welcome through the repository's **Community** tab. When reporting a problem, include the affected example and a short explanation so that it can be reviewed.

---

**Keywords:** logical reasoning dataset, problem solving dataset, SFT dataset, supervised fine-tuning, instruction tuning, reasoning training data, synthetic reasoning data, LLM fine-tuning, filtered dataset, multi-step reasoning.

	---
	pretty_name: "Logical Reasoning & Problem Solving — Filtered SFT Dataset"
	license: gpl-2.0
	language:
	- en
	task_categories:
	- text-generation
	tags:
	- reasoning
	- logical-reasoning
	- logic
	- problem-solving
	- supervised-fine-tuning
	- sft
	- instruction-tuning
	- instruction-following
	- llm
	- language-model-training
	- synthetic-data
	- filtered-data
	- reasoning-dataset
	- multi-step-reasoning
	- analytical-reasoning
	- text
	- datasets
	- gpt-6.1-sol
	annotations_creators:
	- machine-generated
	---

	# Logical Reasoning & Problem Solving — Filtered SFT Dataset

	A filtered, synthetic dataset for supervised fine-tuning of language models on logical reasoning and problem-solving tasks.

	This dataset is intended for developers and researchers training models to interpret problems, reason through constraints, and produce coherent solutions. The examples were generated using GPT-6.1 Sol and subsequently filtered before release.

	Rather than positioning the collection as a general-purpose instruction dataset, it focuses on a specific training objective: improving how language models approach and answer problems that require logical reasoning.

	## At a glance

	\| Property \| Details \|
	\| --- \| --- \|
	\| Primary use \| Supervised fine-tuning (SFT) \|
	\| Focus \| Logical reasoning and problem solving \|
	\| Data origin \| Synthetic examples generated with GPT-6.1 Sol \|
	\| Processing \| Post-generation filtering \|
	\| Modality \| Text \|
	\| License \| GNU General Public License v2.0 (GPL-2.0) \|

	## Why use this dataset?

	Reasoning-oriented fine-tuning benefits from training examples that stay focused on the task. This collection provides a filtered source of synthetic problem-solving data for experiments where reasoning behavior matters more than broad conversational coverage.

	It can be useful for:

	- Reasoning-focused SFT: Adapt instruction-following models to tasks requiring analysis and logical consistency.
	- Problem-solving instruction tuning: Include problem-and-solution examples in a broader fine-tuning mixture.
	- Training-data research: Investigate how filtered synthetic data affects model behavior.
	- Targeted fine-tuning experiments: Compare general instruction tuning with reasoning-focused training mixtures.

	The dataset can be used as a standalone training source or combined with other datasets, depending on the target model and training objective.

	## Data creation and filtering

	The examples were generated with GPT-6.1 Sol and filtered after generation to produce the published collection.

	Filtering is part of the dataset preparation process, but it should not be interpreted as a guarantee that every example is correct, unique, or free of ambiguity. Synthetic reasoning data can contain plausible yet incorrect solutions, incomplete assumptions, and repetitive patterns. Users working on accuracy-sensitive tasks should validate representative samples before training.

	No benchmark gains or independent human-verification results are claimed here.

	## Getting started

	Load the dataset through the Hugging Face `datasets` library:

	```python
	from datasets import load_dataset

	dataset = load_dataset("FlaneAI/GPT-6.1-Sol-Problem-solving-Reasoning-Distilled")
	print(dataset)
	print(dataset[list(dataset.keys())[0]][0])
	```


	For best results, review sample quality, apply the prompt formatting required by your model, and evaluate on an independent reasoning benchmark that is not included in training.

	## Recommended usage

	For a reliable fine-tuning workflow:

	1. Inspect the dataset schema and a representative sample of entries.
	2. Check that the problem statements and target responses match your intended training objective.
	3. Use a consistent chat or instruction template for your base model.
	4. Keep evaluation data separate from the training corpus and check for overlap with known benchmarks.
	5. Measure both task accuracy and general instruction-following quality after fine-tuning.

	## Limitations

	This dataset is synthetically generated, not a collection of independently verified formal proofs. Even after filtering, individual examples may contain reasoning errors, misleading assumptions, or answers that sound convincing without being correct.

	Performance improvements are not guaranteed. Outcomes will depend on the base model, training setup, data mixture, and evaluation methodology. The dataset should not be treated as a standalone benchmark or as ground truth for safety-critical decisions.

	## License

	The dataset is published under the GNU General Public License, version 2.0 (GPL-2.0). Review the license terms before redistribution or reuse. Users are also responsible for checking any applicable rights and terms associated with the underlying generated content and their intended use.

	License reference: https://www.gnu.org/licenses/old-licenses/gpl-2.0.html

	## Feedback and contributions

	Issues, error reports, and practical feedback from fine-tuning experiments are welcome through the repository's Community tab. When reporting a problem, include the affected example and a short explanation so that it can be reviewed.

	---

	Keywords: logical reasoning dataset, problem solving dataset, SFT dataset, supervised fine-tuning, instruction tuning, reasoning training data, synthetic reasoning data, LLM fine-tuning, filtered dataset, multi-step reasoning.