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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

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.

throwaway81523 3y ago
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.
deleted 3y ago
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paulddraper 3y ago
Training uses search, but it plays without search. ChatGPT isn't human, but it was trained with humans.
- karolist 3y ago
  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.
porphyra 3y ago
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).
- Oreb 3y ago
  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.
- mtlmtlmtlmtl 3y ago
  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.
  - epups 3y ago
    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.
    - mtlmtlmtlmtl 3y ago
      Well, NNUE is surprisingly similar to what Stockfish was doing before NNUE. Before it was doing what's called piece-square tables. The basic idea(the stockfish evaluator had a lot more going on in addition, using multiple tables and interpolating between them based on game phase) is to assign some heuristic value to every square, for every piece. So it's just a 6x8x8 array that maps piece positions to values. To get the evaluation of the whole position, you add up all of these mappings for the pieces on the board with opposite signs for the opposing players. If you blur your eyes a little, this already looks a lot like a neural net. It's just a big summation of terms, and if you leave in a 0*(whatever value) for every piece that's not present, you've effectively embedded your lookup table into a giant mathematical expression that can be optimised by gradient descent. The reason computer shogi programmers stumbled on this is that they were experimenting with adding more dimensions to the piece-square table, specifically via indexing by king position as well. So now you have 4 or 5 dimensions, making for a pretty massive array. Hand-tuning all the values becomes less and less feasible, and so I think discovering this idea of rearchitecting it as a neural-net was more or less inevitable. So NNUE is actually just a pretty natural evolution of what they were doing before.

throwaway81523 3y ago

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.

deleted 3y ago

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paulddraper 3y ago

Training uses search, but it plays without search. ChatGPT isn't human, but it was trained with humans.

karolist 3y ago
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.

porphyra 3y ago

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).

Oreb 3y ago
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.
mtlmtlmtlmtl 3y ago
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.
- epups 3y ago
  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.
  - mtlmtlmtlmtl 3y ago
    Well, NNUE is surprisingly similar to what Stockfish was doing before NNUE. Before it was doing what's called piece-square tables. The basic idea(the stockfish evaluator had a lot more going on in addition, using multiple tables and interpolating between them based on game phase) is to assign some heuristic value to every square, for every piece. So it's just a 6x8x8 array that maps piece positions to values. To get the evaluation of the whole position, you add up all of these mappings for the pieces on the board with opposite signs for the opposing players. If you blur your eyes a little, this already looks a lot like a neural net. It's just a big summation of terms, and if you leave in a 0*(whatever value) for every piece that's not present, you've effectively embedded your lookup table into a giant mathematical expression that can be optimised by gradient descent. The reason computer shogi programmers stumbled on this is that they were experimenting with adding more dimensions to the piece-square table, specifically via indexing by king position as well. So now you have 4 or 5 dimensions, making for a pretty massive array. Hand-tuning all the values becomes less and less feasible, and so I think discovering this idea of rearchitecting it as a neural-net was more or less inevitable. So NNUE is actually just a pretty natural evolution of what they were doing before.