Tags: codedeliveryservice/Reckless
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Reckless v0.9.0 (#739) Reckless 0.9.0 is ready! You can download pre-built binaries for Windows, Linux, and macOS from the GitHub Releases page. Thanks to everyone who contributed through code, testing, hardware support, or any other effort. This release came together because of your help. Update highlights NNUE The neuron network has been updated to a modern threat-based architecture. It models the interactions between pieces, where one piece attacks or defends another, and extends the existing king-bucketed piece-square-based inputs with additional features. Multithreading and NUMA support NUMA (Non-Uniform Memory Access) support has been added to Reckless. Worker threads allocate memory and bind to the same NUMA node as the memory they access, reducing the cross-node memory access latency for high thread counts on multi-socket systems. Additionally, more improvements have been made specifically to improve the strength of the multithreading search. MultiPV support Reckless now supports MultiPV (Multiple Principal Variations). It can analyze and display multiple candidate moves, each with its corresponding principal variation. This feature is particularly useful for these who use Reckless for chess analysis as it provides a broader view of possibilities. License The project is now licensed under the GNU Affero General Public License v3.0. The AGPL-3.0 grants the right to use, modify, and distribute it, while requiring that any changes to the source code must be made available under the same license and that distribution of Reckless, including distribution over a network (such as providing access via a web application or service), must also include the full source code (or a pointer to where the source code can be found) to generate the exact binary being used. Full Changelog: v0.8.0...v0.9.0 Playing Strength Reckless v0.9.0 brings a significant Elo gain over the previous version, including in multithreaded settings and (D)FRC variants. The results of the progression are as follows: UHO 40.0+0.40s (https://recklesschess.space/test/11785/) ``` Elo | 62.40 +- 3.31 (95%) Conf | 40.0+0.40s Threads=1 Hash=64MB Games | N: 10000 W: 3435 L: 1658 D: 4907 Penta | [1, 491, 2275, 2196, 37] ``` DFRC 40.0+0.40s (https://recklesschess.space/test/11787/) ``` Elo | 68.13 +- 3.39 (95%) Conf | 40.0+0.40s Threads=1 Hash=64MB Games | N: 10020 W: 3351 L: 1411 D: 5258 Penta | [9, 447, 2229, 2245, 80] ``` SMP 20.20+0.20s (https://recklesschess.space/test/11789/) ``` Elo | 70.91 +- 3.15 (95%) Conf | 20.0+0.20s Threads=8 Hash=512MB Games | N: 10000 W: 3514 L: 1501 D: 4985 Penta | [0, 362, 2279, 2343, 16] ``` New contributors We are excited to welcome new contributors, who have joined the project and made this release possible. Each of them invested time, knowledge, and effort into new features, optimizations, code quality improvements, and Elo gains that have been invaluable. We are grateful for your contributions and look forward to seeing more of your amazing work in the future. Thank you! (in alphabetical order) - @87flowers - @cj5716 - @connormcmonigle - @cosmobobak - @FauziAkram - @MrBrain295 - @protonspring - @SunnyWar Special thanks to all hardware contributors for supporting Reckless' development y providing access to their machines, which is crucial for making progress: https://recklesschess.space/users Binaries Pre-built binaries are provided for Windows, Linux, and macOS, with versions optimized for AVX2, AVX512, and a generic build that runs on virtually all CPUs. Select the binary that matches your operating system (`-windows`, `-linux`, or `-macos`) and your CPU capabilities (`-generic`, `-avx2`, or `-avx512`). On macOS, a single universal build is provided. - Generic builds are the most portable but are significantly slower than AVX2 or AVX512 builds. - AVX2 builds are faster and supported on most modern CPUs. - AVX512 builds are generally the fastest but require a newer CPU. If you're unsure which to use, you can start with the AVX512 build and fall back to AVX2 if you encounter issues. Bench: 3652336
Replace ray permuation computation with a look-up table (AVX512) (#660) Small speedup. VSTC Elo | 1.49 +- 1.19 (95%) SPRT | 4.0+0.04s Threads=1 Hash=16MB LLR | 3.01 (-2.25, 2.89) [0.00, 3.00] Games | N: 83942 W: 21477 L: 21117 D: 41348 Penta | [316, 9246, 22551, 9478, 380] https://recklesschess.space/test/10491/ No functional change. Bench: 3715752
Update default network to v50-441ec316.nnue (#619) STC Elo | 6.86 +- 3.50 (95%) SPRT | 8.0+0.08s Threads=1 Hash=16MB LLR | 2.93 (-2.25, 2.89) [0.00, 3.00] Games | N: 9872 W: 2551 L: 2356 D: 4965 Penta | [22, 1095, 2515, 1274, 30] https://recklesschess.space/test/9756/ LTC Elo | 5.11 +- 2.91 (95%) SPRT | 40.0+0.40s Threads=1 Hash=64MB LLR | 2.89 (-2.25, 2.89) [0.00, 3.00] Games | N: 12862 W: 3223 L: 3034 D: 6605 Penta | [3, 1400, 3437, 1587, 4] https://recklesschess.space/test/9757/ STC DFRC Elo | 6.16 +- 3.11 (95%) SPRT | 8.0+0.08s Threads=1 Hash=16MB LLR | 3.02 (-2.25, 2.89) [0.00, 3.00] Games | N: 10264 W: 1763 L: 1581 D: 6920 Penta | [31, 874, 3148, 1040, 39] https://recklesschess.space/test/9758/ Bench: 3016642
Introduce PCM updates to TT early cutoffs (#615) Elo | 2.28 +- 1.75 (95%) SPRT | 8.0+0.08s Threads=1 Hash=16MB LLR | 2.90 (-2.25, 2.89) [0.00, 3.00] Games | N: 38936 W: 9854 L: 9599 D: 19483 Penta | [80, 4541, 9964, 4810, 73] https://recklesschess.space/test/9576/ Bench: 3652331
Simplify away low impact FDS reduction rules (#567) STC Elo | 3.05 +- 2.76 (95%) SPRT | 8.0+0.08s Threads=1 Hash=16MB LLR | 2.92 (-2.25, 2.89) [-3.00, 0.00] Games | N: 15604 W: 3974 L: 3837 D: 7793 Penta | [26, 1816, 3989, 1937, 34] https://recklesschess.space/test/8941/ LTC Elo | 0.80 +- 2.09 (95%) SPRT | 40.0+0.40s Threads=1 Hash=64MB LLR | 3.00 (-2.25, 2.89) [-4.00, 0.00] Games | N: 24870 W: 6076 L: 6019 D: 12775 Penta | [8, 2858, 6642, 2923, 4] https://recklesschess.space/test/8947/ Bench: 3666261
Release of Reckless v0.8.0 (#474) Reckless has come a long way since its early days as a solo project. During the [FIDE & Google Efficient Chess AI Challenge][1], I worked with Shahin (@peregrineshahin) on the team that finished in second place. After the competition in late February 2025, the whole search algorithm started being rebuilt from the ground up. Shortly after, @peregrineshahin joined the project as one of its co-authors, with Styx (@styxdoto) joining a bit later. Together, we have transformed Reckless into a formidable chess engine, moving far and beyond the capabilities of its predecessor. We are now releasing Reckless v0.8.0, one of the strongest chess engines in the world and the strongest chess engine written in Rust. Playing Strength Reckless v0.8.0 is enormously stronger than the previous release. In practical terms, v0.7.0 is no longer a meaningful opponent of measuring progress. Nevertheless, using a balanced opening book `8moves_v3`, the results of the progression are as follows: STC 8.0+0.08s Elo | 334.77 +- 6.38 (95%) Conf | 8.0+0.08s Threads=1 Hash=16MB Games | N: 10116 W: 7583 L: 38 D: 2495 Penta | [0, 4, 216, 2127, 2711] https://recklesschess.space/test/7421/ LTC 40.0+0.40s Elo | 301.49 +- 8.15 (95%) Conf | 40.0+0.40s Threads=1 Hash=64MB Games | N: 5004 W: 3506 L: 2 D: 1496 Penta | [0, 0, 150, 1200, 1152] https://recklesschess.space/test/7422/ Update highlights Syzygy Tablebase Support We have added support for Syzygy endgame tablebases with up to 7 pieces, thanks to the [Fathom][2] library. Chess960 Support Reckless can now play Chess960 (Fischer Random Chess), with full support for castling rules and position setup. It also handles assymmetrical starting positions, commonly referred to as Double Fischer Random Chess (DFRC). NNUE Improvements The originally used custom network trainer has been replaced with [Bullet][3], a specialized ML library developed by @jw1912. Over 30 iterations of stronger networks have been merged, leading to a multi-layer NNUE model trained on billions of positions. Looking Ahead Since the last release, we have made over 500 commits, and the project remains very much active. We are looking forward to making Reckless better, adding new features, and more! [1]: https://www.kaggle.com/competitions/fide-google-efficiency-chess-ai-challenge [2]: https://github.com/jdart1/Fathom [3]: https://github.com/jw1912/bullet Bench: 2052487 Co-Authored-By: Shahin M. Shahin <41402573+peregrineshahin@users.noreply.github.com> Co-Authored-By: Styx <164851643+styxdoto@users.noreply.github.com>
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