ShengbinYue/paraagent-sft

ParaAgent SFT

10,906 structured tool-use trajectories for learning to explore, plan, and act in parallel.

This is the cold-start supervised fine-tuning dataset released with our paper, ParaAgent: Reinforcing Parallel Acting in Open-World Tool Environments.

Paper · Code

Overview

ParaAgent learns ParaAct, a structured action loop that alternates between exploration and execution. During exploration, the agent searches for needed capabilities through parallel tool retrieval. During execution, it organizes tool dependencies and runs independent calls in parallel.

This dataset provides demonstrations for the first stage of that learning process:

Multi-agent demonstrations → Supervised fine-tuning → Reinforcement learning

The demonstrations are generated by a multi-agent system with shared state, as described in Section 3.3 and Appendix B of the paper. Each record preserves a complete conversation, including the system prompt, task input, assistant reasoning and actions, and environment observations. Together, these trajectories initialize the agent's structured interaction behavior before reinforcement learning.

Dataset Composition

The dataset covers API invocation, MCP interaction, and deep-research tasks. All 10,906 trajectories are provided in a single train split.

Source	Source label	Task modality	Trajectories
ToolBench	toolbench	API invocation	4,471
Toucan	toucan	MCP interaction	4,924
AFM	afm	Deep research	1,511
Total			10,906

See Appendix A.1 of the paper for the source selection and dataset statistics. The source datasets retain their respective ownership and usage terms.

Data Format

The file paraagent-sft.jsonl uses JSON Lines: each line contains one complete trajectory. Newlines within message text are escaped inside JSON strings.

Field	Type	Description
task_id	string	Release-local identifier, numbered "1" through "10906" in file order.
dataset	string	Source label: toolbench, toucan, or afm.
system	string	System prompt for the trajectory.
conversations	list of objects	Ordered messages, each with from and value fields.

Messages use human for task inputs and environment observations, and gpt for assistant outputs. A schematic record is shown below; the text values are placeholders, not an actual training example.

{
  "task_id": "1",
  "dataset": "toolbench",
  "system": "System instructions...",
  "conversations": [
    {"from": "human", "value": "Task instruction..."},
    {"from": "gpt", "value": "Assistant reasoning, plan, and tool actions..."},
    {"from": "human", "value": "Environment observations..."},
    {"from": "gpt", "value": "Assistant continuation or final answer..."}
  ]
}

task_id and dataset are metadata; training uses system and conversations. The IDs are local to this release and should not be used to join records with upstream datasets or ParaAgent RL data.

Getting Started

Load with Hugging Face Datasets

from datasets import load_dataset

train = load_dataset("ShengbinYue/paraagent-sft", split="train")
print(len(train))  # 10906

example = train[0]
print(example["system"])
print(example["conversations"])

Download for ParaAgent training

Run from the root of the ParaAgent code repository:

hf download ShengbinYue/paraagent-sft paraagent-sft.jsonl \
  --repo-type dataset --local-dir data/sft

This places the file at data/sft/paraagent-sft.jsonl, matching the paraagent-sft entry in data/dataset_info.json. The training configuration uses LLaMA-Factory's ShareGPT format with the following mapping:

{
  "paraagent-sft": {
    "file_name": "sft/paraagent-sft.jsonl",
    "formatting": "sharegpt",
    "columns": {"messages": "conversations", "system": "system"},
    "tags": {
      "role_tag": "from",
      "content_tag": "value",
      "user_tag": "human",
      "assistant_tag": "gpt"
    }
  }
}

The provided SFT configuration uses the qwen3_nothink template and a 20,000-token cutoff. With train_on_prompt: false and mask_history: false, the loss covers assistant outputs across all turns, while task inputs and observations are masked. Explicit reasoning in the supplied assistant text is retained; trajectories longer than the cutoff may be truncated during training.

Citation

If you use this dataset in your research, please cite our paper:

@article{yue2026paraagent,
  title={ParaAgent: Reinforcing Parallel Acting in Open-World Tool Environments},
  author={Yue, Shengbin and Wang, Hongru and Wang, Siyuan and Chen, Xiaoxin and Chen, Wei and Wei, Zhongyu},
  journal={arXiv preprint arXiv:2609.33618},
  year={2026}
}