Which it did by doing an inefficient version of the aforementioned 1940s algorithm.
Essentially modern LLMs are able to do normal AI engineering tasks, like running optimization algorithms over weights. The media has been inaccurately describing this as if the AI had some special insight into the weights, when it reality it just performed the tasks an human engineer might do.
Good article! The initial headlines from a few days ago made it seem like frontier models were becoming able to read, understand, and manipulate weights directly. This seemed unlikely to me since they aren’t trained on weights, they’re trained on tokens and task completion (RL). This analysis makes me think a better description is that Astra did some interesting model training research that included running small experiments with altering weights and found a nice edit that fixes some problems. Cool, also cool that this analysis finds a faster approach. But a far cry from the headlines when the paper first came out.
Nice analysis. The simplicity of this indicates that it should be happening during training via regularization.
Does this describe a very powerful LLM, like Astra, editing smaller models?
Could Astra edit itself?
No, it describes a linear optimization algorithm from the 01940s editing a Qwen model with billions of parameters.
"In another experiment, they asked Astra to make Qwen3.5-0.8B better at math by editing its weights"
??
Which it did by doing an inefficient version of the aforementioned 1940s algorithm.
Essentially modern LLMs are able to do normal AI engineering tasks, like running optimization algorithms over weights. The media has been inaccurately describing this as if the AI had some special insight into the weights, when it reality it just performed the tasks an human engineer might do.
Good article! The initial headlines from a few days ago made it seem like frontier models were becoming able to read, understand, and manipulate weights directly. This seemed unlikely to me since they aren’t trained on weights, they’re trained on tokens and task completion (RL). This analysis makes me think a better description is that Astra did some interesting model training research that included running small experiments with altering weights and found a nice edit that fixes some problems. Cool, also cool that this analysis finds a faster approach. But a far cry from the headlines when the paper first came out.
It seems a little like splitting hairs.
Astra didn't directly edit the weights, it came up with a method to edit the weigths?
So, it used another tool to edit the weights?
This kind of seems like saying, I don't directly edit the bits on my hard drive, thus i'm not really the one saving a file?
annnd we can just 'reinvent'(*) the same techniques used to uncensor models for some time.. one step at a time.
(*) scarequotes because the LLMs that suggested these approaches have presumably been exposed to the refusal direction suppression work