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Eysenbach: Scaling RL depth requires combining depth with residual connections

“And if we just made the depth bigger, it makes it worse. If we just add residual connections, it didn't make it better. And it was really this combination of factors that Kevin and Ishan figured out that really made this work.”

Benjamin Eysenbach Dec 31, 2025 ▶ 5:56 [NeurIPS Best Paper] 1000 Layer Networks for Self-Supervised RL — Kevin Wang et al, Princeton

LATENT SPACE Assertion Supported

Eysenbach: Historically, 'deep' RL meant only two to four layers

“So it's like, probably my lab works on deep reinforcement learning, but historically deep meant like two or three or four layers.”

Benjamin Eysenbach Dec 31, 2025 ▶ 2:15 [NeurIPS Best Paper] 1000 Layer Networks for Self-Supervised RL — Kevin Wang et al, Princeton

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