Datasets:
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language:
- en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- text-generation
tags:
- synthetic
- reasoning
- agentic
- tool-use
- multi-turn
- sft
- grpo
pretty_name: Agentic Synth Reasoning
---
# Agentic Synth Reasoning
Synthetic reasoning traces for multi-step agentic behavior, generated incrementally from [nvidia/Nemotron-SFT-Agentic-v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2).
## Dataset Summary
Each record contains a multi-turn agentic conversation with **synthetically generated reasoning traces** (`reasoning_content`) attached to each assistant turn. The reasoning was produced **incrementally** — for each assistant message, only the conversation prefix up to that point was fed to the reasoning model, ensuring **zero future leakage**.
- **3,615 records** across **457 unique domains**
- **17,882 reasoning turns** (99.9% structured format)
- Average **3,002 chars** per reasoning trace
- Tool definitions from the source dataset included in reasoning context
## Generation Method
1. **Source**: Streamed conversations from `interactive_agent`, `tool_calling`, and `search` splits of Nemotron-SFT-Agentic-v2
2. **Incremental reasoning**: For each assistant message at position `i`, only `messages[0..i-1]` + tool definitions were fed to the model — making future leakage physically impossible
3. **Structured format**: Reasoning follows a `### Situation Assessment` → `### Decision Rationale` structure with `●` (confirmed), `◐` (inferred), `○` (uncertain) confidence markers
4. **Temperature escalation retry**: Empty/None responses retried at increasing temperatures (0.7 → 0.85 → 1.0)
5. **Models used**: GLM-5.2 (via Ollama), DeepSeek V4 Flash (via OpenRouter)
## Data Format
```python
{
"messages": [
{"role": "system", "content": "..."},
{"role": "user", "content": "..."},
{"role": "assistant", "content": "...", "reasoning_content": "### Situation Assessment\n● ..."},
{"role": "tool", "content": "...", "tool_call_id": "..."},
...
],
"domain": "theme park attraction management",
"source_dataset": "nvidia/Nemotron-SFT-Agentic-v2",
"original_model": "deepseek/DeepSeek-V3.2",
"num_original_messages": 12,
"num_reasoning_turns": 5,
"generated_at": "2026-07-04T16:55:49.744059+00:00"
}
```
## Validation
| Check | Result |
|---|---|
| Format compliance | 99.9% (17,864/17,882 turns) |
| Future leakage | 0 confirmed (incremental generation) |
| Corrupted fields | 0 |
| Duplicates | 0 |
## Intended Use
Designed as an SFT warmup dataset for GRPO-based LLM evaluator training. The reasoning traces teach models to:
- Decompose user requests in agentic contexts
- Select appropriate tools from available options
- Justify decisions with policy/constraint awareness
- Assess risks and edge cases before acting
## Source Data
Derived from [nvidia/Nemotron-SFT-Agentic-v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2). Original conversations preserved; only `reasoning_content` fields are synthetically generated. |