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Balatro fan claims they trained Google fruit fly brain simulation to beat the game — reinforcement learning currently has the model at 20% success rate A fruit fly may soon beat you at Balatro. Less than two weeks after Google released a mapping of the complete brain and central nervous system of an adult male fruit fly, we've seen enthusiasts put the structure to work everywhere from turning a fruit fly into a day trader to teaching it parallel parking. Now, one Balatro fan says they trained the structure with an algorithm to play the game, with the win rate currently sitting at a cozy 20%. The player shared a sped-up video of the model apparently playing the game. Based on the video, the player chose the lowest difficulty (White Stake) and the default Red Deck. We've already seen OpenAI's GPT-6 'Astra' model beating the game with the Black Deck on Gold Stack difficulty, which is generally considered the hardest combination in the game. ActualAerie1011, the Reddit user who shared the video, says they trained the model using a trainer algorithm they developed to discover useful Balatro seeds. Like other roguelike games, Balatro is randomized, so algorithms like this can discover seeds that are unique and can potentially lead to very high scores (including the game's scoring limit). In order to train the brain, both the brain apparatus (a connectome alongside the actual model) and the algorithm play a seed. Then, the results are compared, and the model on the brain is rewarded or punished based on its choices. Currently, the user says that the brain has a 20% success rate on a random seed, presumably at that same White Stack/Red Deck difficulty. The user says the model doesn't know anything about the seed outside of what's immediately visible on-screen, and that training is ongoing. "The fruit fly will return, strong and smarter," they wrote in a comment on their original post. It's an impressive feat, though some commenters have cast doubt on the project. The player didn't share many details about how they trained the model outside of what's above, nor any repo for the project or references to other open-source projects they used. This isn't uncharted territory for Balatro; projects like BalatroBot and BalatroLLM have been available for about a year. We've reached out to ActualAerie1011 to see if they're able to provide more details on how they trained the model, and we'll update this story when we hear back. Although Balatro seems straightforward enough, it's surprisingly difficult to train a model to play the game, especially at higher difficulties. The core rules of playing and scoring poker hands aren't difficult. However, the complex interactions between jokers (the perks that help you achieve higher scores), how they're ordered and scored, and specific stipulations like boss abilities and temporary/permanent jokers make consistency a high bar to clear, even for human players, much less an AI model. Get Tom's Hardware's best news and in-depth reviews, straight to your inbox. Follow Tom's Hardware on Google News, or add us as a preferred source, to get our latest news, analysis, & reviews in your feeds. Jake Roach is the Senior CPU Analyst at Tom’s Hardware, writing reviews, news, and features about the latest consumer and workstation processors.
document fetched 2026-09-18 02:54 · canonical https://tomshardware.com/tech-industry/artificial-intelligence/balatro-fan-claims-they-trained-google-fruit-fly-brain-simulation-to-beat-the-game-reinforcement-learning-currently-has-the-model-at-20-percent-success-rate