The Ledger, every show
Every statement that passed quotation and attribution checks, across all 44 shows. Pick shows below, then mix any filter with any other.
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Wang: Deep Self-Supervised Method Beats SOTA on Goal-Conditioned RL Significantly
“We do achieve state-of-the-art performance on goal-conditioned RL and Jack's CCRL by a significant amount.”
Kevin Wang: Scaling RL network depth unlocks effective batch size scaling
“We notice that we see that scaling width actually also improves performance, and we also find that actually by scaling depth, we actually unlock the ability to scale along batch size as well.”
Kevin Wang: 1000-layer RL networks can train on single 80GB H100
“The nice thing is that all of our experiments, even the thousand layer networks, can be run on one single, 80 gigabyte, each 100 GPU.”
Wang: Traditional value-based reinforcement learning fails to scale
“And so what we did is that we know that traditional RL, like let's say like value value-based RL doesn't really scale, right? This is pretty clear from the literature.”
Wang: 64 layers saturate performance in most reinforcement learning tasks
“Within our paper, like, for most environments we are able to, like, saturate, like, get to, like, almost perfect performance within just, you know, we don't even need to get to, like, a thousand layers. Like, maybe just 64 layers, for example, is sufficient.”