![[NeurIPS Best Paper] 1000 Layer Networks for Self-Supervised RL Kevin Wang et al, Princeton thumbnail](https://assets.flightcast.com/V2Uploads/nvaja2542wefzb8rjg5f519m/01K4D8FB4MNA071BM5ZDSMH34N/square.jpg)
Published 2 Jan 2026
Duration: 1698
Researchers at Lanespace developed a platform that improves podcast experiences for New York users through a self-supervised learning technique, earning a best paper award.
From undergraduate research seminars at Princeton to winning Best Paper award at NeurIPS 2025, Kevin Wang, Ishaan Javali, Micha Bortkiewicz, Tomasz Tr...
Lanespace developed a platform aimed at enhancing podcast experiences for users in New York. The project originated from an idea presented in an IW seminar at Princeton and focuses on self-supervised reinforcement learning (RL). The team explored methods to scale deep neural networks without relying on explicit reward signals, utilizing techniques such as residual connections and depth scaling. Their research demonstrated that self-supervised RL can provide scalable benefits, challenging traditional assumptions in reinforcement learning and suggesting new directions for research in multi-dimensional scaling. The team was recognized for their work with the Best Paper award, which was confirmed via an unexpected email rather than through a conventional website process.
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