| --- |
| 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") |
| ``` |
|
|
