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News/Features have life history. And we should care
arxiv
PublishedMay 21, 2026 at 4:00 AM
—neutral

Features have life history. And we should care

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arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.18789v1 Announce Type: cross Abstract: Features in language models have life history: they emerge, persist, and die during training, yet the importance of that history remains largely unexplored. We find evidence of a persistent representational backbone, which we identify in Pythia-160M

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Discussion
Mentioned models
02
  • 01
    Pythia-160M
  • 02
    Pythia-410M
Source
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arxiv
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Tags
04
#language models#training dynamics#neural networks#representational learning

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Mentioned models
02
  • 01
    Pythia-160M
  • 02
    Pythia-410M
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#language models#training dynamics#neural networks#representational learning

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arxivOptimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets1darxivJudging the Judges: Confidence and Disagreement in Rubrics as Rewards1darxivContent associations with answer selection and engagement in supply chain community question answering1d
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