<story><title>Diffusion on syntax trees for program synthesis</title><url>https://tree-diffusion.github.io/</url></story><parent_chain></parent_chain><comment><author>zelphirkalt</author><text>This sounds more similar to what people have done with Racket and hint generation for MOOCs. Not sure which university it is again, but I saw a presentation about how they generate hints for students by mutating the syntax tree and analyzing how they had to modify it, to get to a target solution. It was presented at some RacketCon, maybe a decade ago already. Perhaps that knowledge how to do it can be combined with newer machine learning approaches?&lt;p&gt;EDIT: I found the talk: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;invidious.baczek.me&amp;#x2F;watch?v=ijyFC36kVis&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;invidious.baczek.me&amp;#x2F;watch?v=ijyFC36kVis&lt;/a&gt;</text></comment>
<story><title>Diffusion on syntax trees for program synthesis</title><url>https://tree-diffusion.github.io/</url></story><parent_chain></parent_chain><comment><author>pmayrgundter</author><text>It&amp;#x27;s funny, this kind of subtree mutation was looked at pretty deeply by Koza and Adamı in the 90s under the rubric of Genetic Algorithms, but with a slightly different optimization function&lt;p&gt;One ref in the paper to 2000 for GAs for fast generation of program trees, but that&amp;#x27;s missing the main show&lt;p&gt;Hope they&amp;#x27;re reading this and dig into those guys work</text></comment>