---
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - multi-agent
  - supervised-fine-tuning
  - information-retrieval
  - question-answering
pretty_name: WideSeek-R1 SFT Data
configs:
  - config_name: default
    data_files:
      - split: train
        path: sft_width_depth_agent_1p3.jsonl
---

# WideSeek-R1 SFT Data

This dataset contains agent-level, multi-turn supervised fine-tuning trajectories for both width-only and depth-only tasks in WideSeek-R1.

## Construction

The trajectories were generated by [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) using the WideSeek-R1 multi-agent workflow with offline retrieval tools. Width and depth trajectories are balanced at the question level.

For each question-level trajectory, we retain one main-agent session and up to three subagent sessions, giving a target main-to-subagent ratio of 1:3. If fewer than three valid subagents are available, all available subagents are retained. When more than three are available, representative sessions are selected according to their turn lengths.

Each JSONL row represents one complete agent session and may contain multiple assistant turns. The dataset includes 1,903 width trajectories and 1,903 depth trajectories.

## Format

Each row contains:

- `messages`: the complete multi-turn message history for one agent;
- `tools`: the tool definitions available to that agent;
- `metadata`: the task type, agent role, trajectory identifiers, and related information.

```python
from datasets import load_dataset

dataset = load_dataset("WideSeek-R1/WideSeek-R1-SFT-data")
```