Chess AI learns to mimic human play at every skill level,

Chess AI learns to mimic human play at every skill level, from novice to master.

I trained a series of deep learning transformer-based chess models designed to mimic human play, taking inspiration from MAIA and Grandmaster Chess Without Search.

May 13, 20263 min readMachine Learning

The development of transformer-based chess models that can emulate human-like decision-making processes presents a significant advancement in the intersection of artificial intelligence and strategic games. The work showcases a comprehensive approach to training models across various player ratings, from novice to expert, utilizing a staggering amount of data, over one billion games from Lichess. This meticulous effort not only reflects a commitment to enhancing machine understanding of chess but also aligns with the broader trend of leveraging AI to transform traditional domains, akin to innovations seen in financial modeling with tools like Build AI Financial Models in Sourcetable or ETF analysis through ETF Analysis with AI: Compare Funds and Find the Best Investments.

One of the most compelling aspects of this endeavor is the introduction of thinking time models. This innovative approach acknowledges that chess is not merely a game of moves but also a contest of mental endurance and strategy, where time pressure can significantly influence outcomes. By integrating player ratings, clock times, and even the psychological factors associated with decision-making under pressure, these models provide a more nuanced representation of human play. These insights could lead to improved training tools for aspiring chess players, creating a bridge between human intuition and machine learning that empowers users to refine their strategies.

Moreover, the model's performance against benchmarks like MAIA-2 and MAIA-3 indicates a promising trajectory for AI in competitive environments. While the current models excel in accuracy, particularly in lower rating ranges, they fall short of deeper calculations at higher levels. This limitation opens a discussion about the scalability of model complexity in AI and how future iterations could further enhance their capabilities. As chess enthusiasts and players continue to seek innovative ways to analyze and improve their game, advancements like these will play a crucial role in shaping their approach to learning and competition.

Looking ahead, the implications of such advancements extend beyond chess. As we witness AI systems becoming increasingly integrated into various aspects of daily life, the evolution of models that can think and adapt like humans invites us to consider the ethical and practical dimensions of AI's role in society. Will these developments encourage a broader acceptance of AI-driven tools in other areas, such as education or personal productivity? The potential for AI to augment human capabilities is vast, but it also necessitates a thoughtful examination of how we engage with these technologies.

In conclusion, the journey of training transformer-based models to understand and play chess like humans serves as a microcosm for the transformative power of AI across industries. As developers continue to innovate and refine these systems, the chess community—and beyond—stands to benefit from a deeper understanding of both the game and the technology that supports it. The question remains: how will we harness these advancements to not only enhance our experiences but also to foster a future where AI complements human intelligence in meaningful ways?

From Machine Learning

I trained a set of deep learning (transformer-based) chess models to play like humans (inspired by MAIA and Grandmaster Chess Without Search).
There's a separate model for each 100-point rating bucket from ~800 to 2500+. I started with training a mid-strength model from scratch on a 8xH100 cluster, then fine-tuned models for the other rating ranges on my local 5090 GPU. The total training size was nearly a year of Lichess data, about 1B total games.

Explore how AI assistance helped build a chess engine from home

I built Autochess NN, a browser-playable neural chess engine that started as a personal experiment in understanding AlphaZero-style systems by actually building one end to end. This project was unapologetically vibecoded - but not in the “thin wrapper around an API” sense. I used AI heavily as a research/coding assistant in a Karpathy-inspired autoresearch workflow: read papers, inspect ideas, prototype, ablate, optimize, repeat. The interesting part for me was seeing how far that loop could go on home hardware (just ordinary gaming RTX 4090). Current public V3: residual CNN + transformer learned thought tokens ~16M parameters 19-plane 8x8 input 4672-move policy head + value head trained on 100M+ positions pipeline: 2200+ Lichess supervised pretraining -> Syzygy endgame fine-tuning -> self-play RL with search distillation CPU inference + shallow 1-ply lookahead / quiescence (below 2ms) I also wrapped it in a browser app so the model is inspectable, not just benchmarked: play vs AI, board editor, PGN import/replay, puzzles, and move analysis showing top-move probabilities and how the “thinking” step shifts them. What surprised me is that, after a lot of optimization, this may have ended up being unusually compute-efficient for its strength - possibly one of the more efficient hobbyist neural chess engines above 2500 Elo. I’m saying that as a hypothesis to pressure-test, not as a marketing claim, and I’d genuinely welcome criticism on evaluation methodology. I’m now working on V4 with a different architecture: CNN + Transformer + Thought Tokens + DAB (Dynamic Attention Bias) @ 50M parameters For V5, I want to test something more speculative that I’m calling Temporal Look-Ahead: the network internally represents future moves and propagates that information backward through attention to inform the current decision. Demo: https://games.jesion.pl Project details: https://games.jesion.pl/about Price: free browser demo. Nickname/email are only needed if you want to appear on the public leaderboard. The feedback I’d value most: Best ablation setup for thought tokens / DAB Better methodology for measuring Elo-vs-compute efficiency on home hardware Whether the Temporal Look-Ahead framing sounds genuinely useful or just fancy rebranding of something already known Ideas for stronger evaluation against classical engines without overclaiming Cheers, Adam submitted by /u/Adam_Jesion [link] [comments]