Comment by fiforpg
8 months ago
Given that they used position evaluation from (a search chess engine[1]) Stockfish, how is this "without search"?
Edit: looking further than the abstract, this is rather an exploration of scale necessary for a strong engine. Could go without "without search" in the title I guess.
[1]: IIRC, it also uses a Leela-inspired NN for evaluation.
Leela without search supposedly plays around expert level, but I thought the no-search Leela approach ran out of gas around there. Without search there means evaluating 1 board position per move. The engine in the paper (per the abstract) use a big LLM instead of a Leela style DCNN.
Training uses search, but it plays without search.
ChatGPT isn't human, but it was trained with humans.
So it's a space time trade-off then? Store enough searched and weighted positions into the model and infer them. In this way, inference is replacing Stockfish search, just less accurately, but much faster and with memory required for the model.
Does Stockfish really use a Leela-inspired NN? I thought the NNUE was independently developed and completely different (it's a very tiny network that runs on the CPU).
Yeah, NNUE is a separate invention that unfortunately, Deepmind often get undeserved credit for inspiring. It didn't even originate in chess engines but a shogi version of Stockfish. Architecture is completely different from the nets in Leela or Alpha Zero.
Wait, so progress on Stockfish would happen regardless of Alpha Chess? I always thought they were inspired by it in the newer versions, and got much improved rating from incorporating it.
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This is true, but at least for a while (I’m not sure if it’s still the case), Leela data was used (along with data generated from Stockfish self-play) to train Stockfish’s NN.