Datasets:

mkurman
/

agentic-synth-reasoning

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.

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

Source: Streamed conversations from interactive_agent, tool_calling, and search splits of Nemotron-SFT-Agentic-v2
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
Structured format: Reasoning follows a ### Situation Assessment → ### Decision Rationale structure with ● (confirmed), ◐ (inferred), ○ (uncertain) confidence markers
Temperature escalation retry: Empty/None responses retried at increasing temperatures (0.7 → 0.85 → 1.0)
Models used: GLM-5.2 (via Ollama), DeepSeek V4 Flash (via OpenRouter)

Data Format

{
    "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. Original conversations preserved; only reasoning_content fields are synthetically generated.