SkillRL-SFT-Data/README.md

metadata

dataset_info:
  - config_name: alfworld
    features:
      - name: instruction
        dtype: string
      - name: output
        dtype: string
    splits:
      - name: train
        num_bytes: 42191393
        num_examples: 7486
    download_size: 5617790
    dataset_size: 42191393
  - config_name: search
    features:
      - name: instruction
        dtype: string
      - name: output
        dtype: string
    splits:
      - name: train
        num_bytes: 6659585
        num_examples: 1214
    download_size: 1050795
    dataset_size: 6659585
  - config_name: webshop
    features:
      - name: instruction
        dtype: string
      - name: output
        dtype: string
    splits:
      - name: train
        num_bytes: 12351072
        num_examples: 2553
    download_size: 1652500
    dataset_size: 12351072
configs:
  - config_name: alfworld
    data_files:
      - split: train
        path: alfworld/train-*
  - config_name: search
    data_files:
      - split: train
        path: search/train-*
  - config_name: webshop
    data_files:
      - split: train
        path: webshop/train-*
license: mit
language:
  - en
tags:
  - reinforcement-learning
  - embodied-ai
  - instruction-following
  - SFT
  - agent
size_categories:
  - 10K<n<100K

SkillRL-SFT-Data

This is the Supervised Fine-Tuning (SFT) dataset used in the paper SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning.

SkillRL-SFT-Data provides instruction-output pairs for training base agent policies on three interactive decision-making environments: ALFWorld, WebShop, and Search. Each example contains a structured instruction with retrieved skill context from the hierarchical SkillBank and the corresponding expert action output.

Dataset Summary

Config	Environment	Examples	Description
alfworld	ALFWorld	7,486	Embodied household tasks (pick & place, clean, heat, cool, examine, etc.)
webshop	WebShop	2,553	Web-based shopping navigation tasks
search	Search	1,214	Multi-step web search QA tasks (NQ, TriviaQA, PopQA, HotpotQA, 2Wiki, MuSiQue, Bamboogle)

Total: 11,253 instruction-output pairs.

Data Format

Each example contains two fields:

• instruction: A detailed prompt including the task goal, retrieved relevant experience from the hierarchical SkillBank (general principles, task-specific skills, and mistakes to avoid), current environment observation, and admissible actions.
• output: The expected agent response, consisting of a step-by-step reasoning process wrapped in <think> tags followed by the selected action in <action> tags.

Usage

from datasets import load_dataset

# Load a specific config
alfworld_data = load_dataset("Jianwen/SkillRL-SFT-Data", "alfworld")
webshop_data = load_dataset("Jianwen/SkillRL-SFT-Data", "webshop")
search_data = load_dataset("Jianwen/SkillRL-SFT-Data", "search")

Related Models

Environment	SFT Checkpoint	RL Checkpoint
ALFWorld	Alfworld-7B-SFT	Alfworld-7B-RL
WebShop	Webshop-7B-SFT	Webshop-7B-RL
Search	Search-7B-SFT	Search-7B-RL

All models are fine-tuned from Qwen2.5-7B-Instruct. SFT checkpoints are trained on this dataset; RL checkpoints are further optimized via recursive skill-augmented reinforcement learning.

Related Resources

• Paper: SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
• Code: https://github.com/aiming-lab/SkillRL

Citation

@article{xia2026skillrl,
  title={SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning},
  author={Xia, Peng and Chen, Jianwen and Wang, Hanyang and Liu, Jiaqi and Zeng, Kaide and Wang, Yu and Han, Siwei and Zhou, Yiyang and Zhao, Xujiang and Chen, Haifeng and others},
  journal={arXiv preprint arXiv:2602.08234},
  year={2026}
}