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sub:assertion {
  <https://arxiv.org/abs/2406.12208> <https://sense-nets.xyz/hasZoteroItemType> "preprint" .
  
  sub:assertion dcterms:creator <https://w3id.org/np/RAoSadUw99CeqDlR2400018nqTzR_38fT86OrTzk16Vts>;
    <http://purl.org/spar/cito/discusses> <https://x.com/LChoshen/status/1729488495515713672>,
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    rdfs:comment """ Evolver, model merging in a genetic algorithm
Improves on current merging techniques (my beloved TIES 🫣 )
Train diverse models
Merge regularly or take diff between two models
Update some parameters
Keep if good
Repeat

https://arxiv.org/abs/2406.12208
@jingli9111 @banting_liu @576gsk https://twitter.com/LChoshen/status/1803410440535326786/photo/1

 Merging is aimed at taking many models and getting one that generalizes better, there are various methods for it, read more e.g. on TIES
https://x.com/prateeky2806/status/1665759148380758022

 Genetic algorithms evolve models, in steps:
Create mutations (here new m = m_old + a(m_1-m_2)) m are models a some constant

Crossover, take some of the mutation and apply it, for each parameter randomly keep m_old or update to m_new

Survive, keep only the best performing on val

 By sometimes merging and sometimes evolving (and dev sets) they improve over all current methods https://twitter.com/LChoshen/status/1803410445635653960/photo/1

 In some sense, this can be seen as a better search in the region between the merged models, which we know is not equally good but all better than the edges
https://x.com/LChoshen/status/1729488495515713672 https://twitter.com/LChoshen/status/1803410447246250483/photo/1

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