LLMs can read and write neural network weights
Original title: [AINews] LLMs can read & write neural network weights: "Weight Space Programming" cuts Autoresearch out of the RSI loop
Research shows LLMs can directly read and edit neural network weights, enabling autonomous model improvement that cuts traditional autoresearch out of the loop.
A new paper demonstrates that sufficiently capable LLMs can read and write the weights of smaller models, allowing them to debug, fix bugs and even write new networks from scratch in one shot. The capability was observed across models from Anthropic, OpenAI and xAI. It bypasses the need for writing PyTorch code and running experiments, marking a step beyond previous autoresearch demonstrations.
- LLMs read and edit model weights directly
- Can fix bugs and improve models in 50 steps
- Beats some independently trained models
- Applies to simple games and RL policies
- Independent reproductions found
From the original article
14 min read at latent.space
Editing Qwen’s weights to reduce reasoning loops, DeepSeek’s super-agent prototype, and the GDPx benchmark.
By swyx .ai · latent.space
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