Dataset · Hugging Face

osunlp/early-experience

Name: early-experience
Creator: osunlp
License: mit
Keywords: task_categories:reinforcement-learning, language:en, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:polars, library:mlcroissant, arxiv:2510.08558, region:us, agent, early-experience, imitation-learning

Early Experience — Reproduction Data Supervised fine-tuning data for reproducing Agent Learning via Early Experience across 8 agent environments. Each environment provides data for three training paradigms: IL — Imitation Learning: expert SR — Self-Reflection: expert + reflection IWM — Implicit World Modeling: iwm (world model) → expert Code: OSU-NLP-Group/EarlyExperience Usage from datasets import load_dataset # load_dataset("osunlp/early-experience"… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/early-experience.

View on Hugging FaceReport an issue

Author / organization	osunlp
License (as declared)	mit
Downloads	351 (as reported by Hugging Face)
Likes	9
Created	6 July 2026
Last updated at source	7 July 2026

agentearly-experienceimitation-learning

Embed this entry

<iframe src="https://silytics.si/embed/item/f626ec73-acb8-4e39-ab35-9c21a58057fc" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Popularity, last 3 days

351 now (-21 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.

More from osunlp

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.