Searchless_chess Alternatives

searchless_chess reviews and mentions

• OpenAI o3 and o4-mini – OpenAI
  7 projects | news.ycombinator.com | 16 Apr 2025

  >These models cannot even make legal chess moves. That’s incredibly basic logic, and it shows how LLMs are still completely incapable of reasoning or understanding.

  Yeah they can. There's a link I shared to prove it which you've conveniently ignored.

  LLMs trained on chess games play chess just fine. They don't make silly mistakes and they very rarely make illegal moves.

  There's gpt-3.5-turbo-instruct which i already shared and plays at around 1800 ELO. Then there's this grandmaster level chess transformer - https://arxiv.org/abs/2402.04494

  So are they capable of reasoning now or would you like to shift the posts ?

• Grandmaster-Level Chess Without Search
  9 projects | news.ycombinator.com | 17 Oct 2024

  This repository provides an implementation of our paper Grandmaster-Level Chess Without Search. https://arxiv.org/abs/2402.04494

  The recent breakthrough successes in machine learning are mainly attributed to scale: namely large-scale attention-based architectures and datasets of unprecedented scale. This paper investigates the impact of training at scale for chess. Unlike traditional chess engines that rely on complex heuristics, explicit search, or a combination of both, we train a 270M parameter transformer model with supervised learning on a dataset of 10 million chess games. We annotate each board in the dataset with action-values provided by the powerful Stockfish 16 engine, leading to roughly 15 billion data points. Our largest model reaches a Lichess blitz Elo of 2895 against humans, and successfully solves a series of challenging chess puzzles, without any domain-specific tweaks or explicit search algorithms. We also show that our model outperforms AlphaZero's policy and value networks (without MCTS) and GPT-3.5-turbo-instruct. A systematic investigation of model and dataset size shows that strong chess performance only arises at sufficient scale. To validate our results, we perform an extensive series of ablations of design choices and hyperparameters.