Princeton researchers train a 4B LLM to reach 2700 Elo in chess (with no signs of a plateau when they stopped training) and can explain its moves accurately. They say the training technique can also be applied to other games, robotics, and computer use

Princeton researchers trained a 4B parameter LLM to achieve a 2700 Elo rating in chess through specialized training techniques. The model generates accurate natural language explanations for its moves, demonstrating reasoning capabilities that may generalize to robotics and computer use tasks.