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LeMario: Super Mario Bros trained on a JEPA Model
Benjamin Bai
·
July 2026
·
LeWorldModel paper
·
GitHub
I wanted to reproduce
LeWorldModel
, a small Joint-Embedding Predictive Architecture (JEPA) that learns world dynamics from pixels and actions. The original paper used it for reward-free planning in Push-T. But, since I loved video games, and at the same time wanted to learn more deeply about LeCun's JEPA architecture, I decided to write the whole architecture from scratch and train it on Super Mario Bros.
The model passed every test I initially thought mattered. It generalized to held-out episodes, used the actions, and predicted five-step futures better than strong baselines. Raw reward-free planning could move Mario toward nearby image goals and finish within two a (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://www.benjamin-bai.com/projects/lemario)获取最准确的信息。

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🔗 **原文链接**: [LeMario: Training a JEPA World Model on Super Mario Bros](https://www.benjamin-bai.com/projects/lemario)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 15票 · 👤 kevinjosethomas

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🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-07-15 08:01