SAC

topic on 1 show · 1 statements across 1 episodes · said 1 times in 1 episodes since 2025

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Eysenbach: 1,000-layer RL requires reward-free objectives, not just architectural tricks
“I think the main conclusion is that using big networks not only requires these architectural tricks, but also, as Kevin mentioned before, it requires using a different objective. This objective doesn't actually use rewards in it, and so there's another word in…”
Benjamin Eysenbach Dec 31, 2025 ▶ 8:08 [NeurIPS Best Paper] 1000 Layer Networks for Self-Supervised RL — Kevin Wang et al, Princeton

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