LLMs Directly Read and Write Neural Network Weights: Weight Space Programming Breakthrough
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On November 3, 2026, Latent Space and Alex Zhang reported a finding: having a sufficiently smart LLM directly read and edit the weights of weaker models enables autonomous model training without humans writing PyTorch/JAX code or any manual intervention. The method, called "Weight Space Programming," has reportedly been independently reproduced. According to the report, the method currently only works in simple settings such as simple games and RL policies, and has not yet scaled to more complex tasks. However, as a new capability related to RSI (recursive self-improvement), it represents a zero-to-one breakthrough. The report predicts that, given its significant efficiency advantages, the method will quickly be taken up for research and exploration. This is currently the only report on the event, with no follow-up developments or contradictory reports.
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- Latent Space[AINews] LLMs can read & write neural network weights: "Weight Space Programming" cuts Autoresearch out of the RSI loop
Latent Space and Alex Zhang report a finding: sufficiently smart LLMs can directly read and edit the weights of dumber models, autonomously training models without humans writing PyTorch/JAX code or intervening, and the result has been independently reproduced. The method currently only works for simple games and RL policies and has not yet scaled to more complex tasks, but as a new RSI capability it marks a zero-to-one breakthrough, and it is expected to be quickly put into research and exploration due to its significant efficiency.
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