X-Git-Url: https://git.sesse.net/?p=stockfish;a=blobdiff_plain;f=README.md;h=330d19edd1f666ca90296fe0cdabe83b27e2c230;hp=79db8170257dfd66cabb4a997adc054b66641ed3;hb=4b86ef8c4f8755850b38f2eca026cb9da20c4d01;hpb=64f21ecdae0402bfde4e9f4e61097c99bcae343e

diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -175,8 +175,12 @@ on the evaluations of millions of positions at moderate search depth.
 The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward.
 It can be evaluated efficiently on CPUs, and exploits the fact that only parts
 of the neural network need to be updated after a typical chess move.
-[The nodchip repository](https://github.com/nodchip/Stockfish) provides additional
-tools to train and develop the NNUE networks. On CPUs supporting modern vector instructions
+[The nodchip repository](https://github.com/nodchip/Stockfish) provided the first version of
+the needed tools to train and develop the NNUE networks. Today, more advanced training tools are available
+in [the nnue-pytorch repository](https://github.com/glinscott/nnue-pytorch/), while data generation tools
+are available in [a dedicated branch](https://github.com/official-stockfish/Stockfish/tree/tools).
+
+On CPUs supporting modern vector instructions
 (avx2 and similar), the NNUE evaluation results in much stronger playing strength, even
 if the nodes per second computed by the engine is somewhat lower (roughly 80% of nps
 is typical).