---
language:
- en
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- text-generation
pretty_name: Agentic DPO Tool-Use Pairs (3K)
tags:
- dpo
- preference-pairs
- tool-use
- agentic
- rlhf
- function-calling
- alignment
- synthetic
configs:
- config_name: default
  data_files:
  - split: train
    path: agentic-dpo-tool-use-3k.jsonl
---

# Agentic DPO Tool-Use Pairs (3K)

Synthetic DPO preference pairs for training LLMs to use tools correctly in agentic settings.

## Dataset Description

**3,000 preference pairs** covering 7 tool categories:
- `web_search` — real-time web search
- `calculator` — mathematical expression evaluation
- `weather_api` — current weather retrieval
- `code_interpreter` — Python code execution
- `database_query` — SQL database queries
- `stock_price` — financial data lookup
- `translate` — multilingual translation

Each example contains:
- `system`: system prompt with tool definitions
- `prompt`: user query requiring tool use
- `chosen`: correct response that uses the appropriate tool and interprets results
- `rejected`: flawed response (refuses tool use / hallucinates / ignores tool / claims no access)
- `metadata`: category and contextual info

## Format

```json
{
  "system": "You are a helpful AI assistant with access to the following tools: ...",
  "prompt": "What's the current price of NVDA?",
  "chosen": "<tool_call>{...}</tool_call>\n<tool_result>{...}</tool_result>\n**NVDA** is trading at ...",
  "rejected": "NVDA stock is around $875 but I don't have real-time data...",
  "metadata": {"category": "stock_price", "ticker": "NVDA"},
  "id": "abc123def456"
}
```

## Use Case

Fine-tune models to:
1. **Recognize** when a tool is needed vs. when to answer from knowledge
2. **Call tools correctly** with proper parameters
3. **Interpret and present** tool results clearly

Compatible with DPO, IPO, KTO, and other preference optimization frameworks.

## License

Apache 2.0