Maybe this is what the author is getting at, but if LP solvers are useful tools for editing weights, then Astra access to this methodology might make it more effective at weight editing.

The interpretation here is not so much that an LP can do Astra's work -- after all the Astra prompt was "improve Qwen's math by changing weights" or similar. Instead, it's that with some guidance (eliminate this behavior, preserve that behavior, as expressed through linear constraints) the solver is much more efficient than Astra's trial and error. So essentially the solver would be a way of cutting through all the time consuming stuff and expressing Astra's intent more immediately. And then presumably Astra could iterate on that to a better solution.

The use of an LP solver here is a really cool insight.

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?

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.

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