Datasets:

nphearum
/

grpo-4k-reasoning-tools

@@ -24,8 +24,6 @@ size_categories:
 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:
@@ -40,8 +38,6 @@ It is suitable for training or fine-tuning models to:
 * Decide when to use tools
 * Produce structured and reliable outputs
----
 ## 🎯 Tasks Covered
 This dataset spans multiple NLP task categories:
@@ -53,8 +49,6 @@ This dataset spans multiple NLP task categories:
 Each example is designed to encourage **reasoning-first behavior**, often requiring intermediate steps before producing the final answer.
----
 ## ✨ Key Features
 * **🧠 Reasoning-Centric**
@@ -72,14 +66,13 @@ Each example is designed to encourage **reasoning-first behavior**, often requir
 * **⚙️ Structured Outputs**
   Useful for training structured generation (JSON, tool calls, etc.).
----
 ## 📂 Dataset Structure
 Each sample typically contains:
 ```json
 {
   "system": "User query or task description",
   "user": "Optional context",
   "thinking": "Step-by-step reasoning process",
@@ -103,30 +96,24 @@ Each sample typically contains:
 | `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)
@@ -139,49 +126,35 @@ 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
@@ -193,10 +166,7 @@ Feel free to:
 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)

 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:
 * Decide when to use tools
 * Produce structured and reliable outputs
 ## 🎯 Tasks Covered
 This dataset spans multiple NLP task categories:
 Each example is designed to encourage **reasoning-first behavior**, often requiring intermediate steps before producing the final answer.
 ## ✨ Key Features
 * **🧠 Reasoning-Centric**
 * **⚙️ 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",
 | `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)
 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
 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)