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Simon Willison Tests Qwen 3.8 27B on Game Identification from Model Weights

1 reports 1 reporting sources Updated 2 hours ago

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Simon Willison found that Qwen 3.8 27B, running on his laptop, identified Pac-Man, Pong, and Breakout from neural network weights, but mistook Freeway for a Pac-Man-like game. The prompts asked which grid game a policy network had learned, using examples from a paper’s GitHub repository; the model had no tools or code execution. Willison said the test does not show that source code is becoming obsolete. He distinguished identifying a game from learned weights from using code to update models.

Generated by AI from reports · Updated 1 hours ago

Latest development11/4 00:29 (UTC+8)
Simon Willison Tests Whether Qwen 3.8 27B Can Identify Games from Model WeightsSimon Willison

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11/4
  1. Simon Willison
    Simon Willison Tests Whether Qwen 3.8 27B Can Identify Games from Model Weights

    Simon Willison reports that Qwen 3.8 27B running on his laptop identified Pac-Man, Pong, and Breakout from neural network weights, but mistook Freeway for a Pac-Man-like game. The prompts came from a paper’s accompanying GitHub repository and asked models to identify which grid game a policy network had learned, with no tools or code execution. Willison distinguishes this task from using code to update models and says the results do not justify claims that source code is becoming obsolete.

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