Luna Chess-Engine (LCE) 4.0.0 - new version chess engine

Luna Chess Engine
Luna is a high-performance chess engine written in Rust, designed for efficiency and strength. It combines classical bitboard-based move generation with a state-of-the-art NNUE (Efficiently Updatable Neural Network) evaluation, allowing for deep positional understand in.

Author: Spunc595

Luna Chess-Engine (LCE) JA what's new?

A network trained by the author on public data

This is the first release whose embedded network (resources/net.bin) was

trained by the author, with his own training pipeline, on public Leela Chess

Zero data. It was trained with bullet ,on 1 billion positions (about 914 million unique by exact board and side-to-move match; 8 epochs) in the king-bucketed 768x4 mirrored

architecture ported from akimbo: 1024 hidden neurons, squared clipped ReLU.

The file format and size are unchanged.

The architecture and the AVX2 inference kernel are still akimbo's. Only the

trained weights changed.

Training data

Leela Chess Zero self-play games (runs T77 and T79), as filtered and

converted to bullet's binary format by Linrock (Hugging Face:

linrock/bullet-training-data, subset S2; the conversion carries no separate

license declaration). The Lc0 project announced in June 2021 that its

training data is released under the Open Database License 1.0 and the

Database Contents License 1.0

(https://lczero.org/blog/2021/06/the-importance-of-open-data/). The network

weights are a trained model, not a redistribution of that data; how the

ODbL's share-alike terms apply to trained weights is not settled by the

license text, and this release makes no claim either way. Contains

information from the Leela Chess Zero training data, available under the

ODbL 1.0.

Strength

Measured in Luna's own search against akimbo's network (the one shipped

through v3.1.7): 2,000 games at 10+0.1, same settings on both sides apart

from the network and its output scale.

+492 =1018 −490, +0.3 ± 10.7 Elo (95% CI), no losses on time.

The two networks are statistically indistinguishable: the interval runs from

roughly −10 to +11 Elo. This is a single training run, and v4.0.0 should be

expected to play at about the strength of v3.1.7, not stronger.

Output scale: SCALE = 358

This training recipe (a WDL ramp) produces outputs about 1.116 times larger

than akimbo's network. The factor was measured twice, on networks of two

different architectures trained with the same recipe: 1.1156 and 1.1164.

SCALE is therefore 358 instead of 400 (400 / 1.1156). On the earlier

network, applying this correction was worth +15.9 Elo (SPRT, 1,443 games,

LOS 99.5%). It is applied in the engine rather than fixed in training; the

WDL ramp is to be revisited in the next training run.

Measured along the way

All in Luna's own search, against akimbo's network unless stated:

A first network trained by the author without king buckets lost about 50

Elo (−49.8 ± 10.9, 2,000 games, material scale factor off on both sides)

and was not shipped.

Correcting its output scale: +15.9 ± 11.9 Elo (SPRT, 1,443 games).

Adding the 768x4 king buckets: +36.9 ± 20.5 Elo over that network

(SPRT, 529 games).

The shipped network against akimbo's: +0.3 ± 10.7 Elo.

Not yet measured

The material scaling factor introduced in v3.1.7 and the search's

centipawn-denominated margins were not re-tuned for this network; they stay

as they were in v3.1.7. Follow-up measurements are planned.

Luna Chess-Engine (LCE) 4.0.0 download   mirror

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