ResNets outperform Transformers at low budget
Eric Jang found that for small data regimes in Go, ResNets outperform Transformers, providing better bang for the buck at lower compute budgets.
Where this was said
What the neural network does
At 33:42 · chapter starts 32:04
Eric describes the neural network architecture — ResNet or Transformer, two output heads (policy and value) — and explains how training on expert human games produces a strong Go player before any search. [1] — Eric Jang "AlphaGo's neural network does two things: predict who's going to win (value head), and predict which moves are worth trying (policy head). …" 32:00
200–2048 simulations per move
During AlphaGo training, MCTS uses between 200 and 2048 simulations per move; the AlphaGo Lee match used tens of thousands of simulations per move.
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