Qwen 3.8 27B on my laptop can interpret model weights, too (simonwillison.net)

Article summary

The article discusses a recent paper on Weight-Space Programming, which shows that large language models (LLMs) can interpret and modify the raw weights of a machine learning model. The author replicates one of the experiments from the paper using Qwen 3.8 27B on their laptop, demonstrating its ability to understand model behaviors from weights. The paper's findings have sparked interesting discussions about the potential of LLMs to edit their own model weights. The author remains skeptical about the broader implications of this technology, citing the need for more structured text and explicit documentation.

Main themes

• Weight-Space Programming
• LLM capabilities
• Model interpretability
• AI development
• Machine learning

What commenters say

• Some commentators believe that LLMs can optimize general intelligence without external validation, while others argue that this is not possible without benchmarks and validation.
• The ability of LLMs to interpret model weights is seen as a significant advancement, but some commentators question its practical applications and potential impact on the field of AI.
• There is disagreement about whether the age of source code is coming to an end, with some commentators arguing that incomprehensible machines editing their own model weights is a likely future scenario, while others are more skeptical.
• Some commentators think that the technology is not surprising, given the high-dimensionality linear algebra capabilities of LLMs, and that it is a natural extension of their ability to process and generate text.
• The question of whether a blog post or pre-print should be referred to as a 'paper' is raised, with some commentators arguing that it should only be considered a paper after peer review and acceptance for publication.
• The potential benefits of text-to-weights include reduced training time and cost, as well as the possibility of instant fine-tunes and highly optimized iteration cycles.
• Some commentators are excited about the potential of LLMs to edit their own model weights, seeing it as a way to improve model performance and efficiency, while others are more cautious and emphasize the need for further research and development.