nphearum/grpo-4k-reasoning-tools

---
license: apache-2.0
task_categories:
- question-answering
- feature-extraction
- text-classification
- summarization
language:
- en
tags:
- thinking
- reasoning
- tools
- grpo
- function-calling
- Opus
- gpt
- qwen
size_categories:
- 1K<n<10K
---

# 🧠 GRPO 4K Reasoning Tools

A compact, high-quality dataset designed to train and evaluate **reasoning-capable LLMs with tool usage and function calling**. This dataset focuses on structured thinking, multi-step reasoning, and practical tool integration across diverse NLP tasks.

## 📌 Overview

**GRPO 4K Reasoning Tools** is a curated dataset of ~4K examples that combines:

* 🧩 Step-by-step reasoning (“thinking” traces)
* 🛠️ Tool usage / function calling patterns
* 🧠 Multi-task learning signals

It is suitable for training or fine-tuning models to:

* Think before answering
* Decide when to use tools
* Produce structured and reliable outputs

## 🎯 Tasks Covered

This dataset spans multiple NLP task categories:

* **Question Answering**
* **Feature Extraction**
* **Text Classification**
* **Summarization**

Each example is designed to encourage **reasoning-first behavior**, often requiring intermediate steps before producing the final answer.

## ✨ Key Features

* **🧠 Reasoning-Centric**
Includes explicit reasoning steps to improve chain-of-thought capabilities.

* **🛠️ Tool-Augmented**
Examples demonstrate when and how to call tools (function calling format).

* **🔄 Multi-Model Friendly**
Compatible with training setups for models like GPT-style, Qwen, and other instruction-tuned LLMs.

* **📏 Compact but Dense (~4K samples)**
Carefully curated for quality over quantity.

* **⚙️ Structured Outputs**
Useful for training structured generation (JSON, tool calls, etc.).

## 📂 Dataset Structure

Each sample typically contains:

```json
{
"category": "math"
"system": "User query or task description",
"user": "Optional context",
"thinking": "Step-by-step reasoning process",
"messages": [
{
"role": "assistant",
"content": "<think>...</think>..."
}
],
"assistant": "Final answer"
}
```

### Fields Explained

| Field | Description |
| ------------- | ----------------------------------- |
| `instruction` | Task description or user query |
| `input` | Additional context (optional) |
| `thinking` | Intermediate reasoning steps |
| `tool_calls` | Function/tool usage (if applicable) |
| `output` | Final response |

## 🧪 Use Cases

* Fine-tuning LLMs for:
* Tool use (function calling)
* Structured reasoning
* Multi-step problem solving

* Evaluating:
* Reasoning quality
* Tool selection accuracy
* Output consistency

* Research in:
* GRPO (Generalized Reinforcement Policy Optimization)
* Chain-of-thought learning
* Tool-augmented agents

## 🚀 Getting Started

### Load Dataset (Hugging Face)

```python
from datasets import load_dataset

dataset = load_dataset("nphearum/grpo-4k-reasoning-tools")

print(dataset["train"][0])
```

## 🧠 Training Tips

* Use **reasoning traces (`thinking`)** as supervision targets
* Optionally:
* Mask reasoning during inference
* Train with or without tool calls depending on your objective

* Combine with:
* Instruction tuning datasets
* Tool-use benchmarks

## 🏷️ Tags

`reasoning` • `thinking` • `tools` • `function-calling` • `grpo` • `multi-task`

## 📊 Dataset Size

* **~4K examples**
* Category: `1K < n < 10K`

## 📜 License

Apache License 2.0

## 🤝 Contributing

Contributions, improvements, and extensions are welcome!
Feel free to:
* Add more tool-use scenarios
* Improve reasoning quality
* Expand task diversity

---

## 📬 Contact

Maintained by **@nphearum**
For questions or collaboration, open an issue or discussion on the repository.

If you want, I can also tailor this README for:
* Hugging Face dataset card format (with YAML + metrics)
* GitHub repo README (with badges, visuals)
* Training pipeline examples (GRPO / RLHF / SFT)

Just tell me 👍