Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT - WSJ
<a href="https://news.google.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?oc=5" target="_blank">Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT</a> <font color="#6f6f6f">WSJ</font>
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Grokking Beyond Addition
Hi everyone, I’m excited to share my research paper: “Grokking Beyond Addition: Circuit-Level Analysis of Algebraic Learning in Transformers” Paper: https://zenodo.org/records/19256207 This work explores grokking across multiple algebraic structures and shows a clear result: At small model scale (d_model = 64), transformers reliably grok abelian tasks but fail to generalize on non-abelian groups , even with 100% training accuracy. It also highlights: Early circuit formation before generalization Evidence for discrete-log structure in multiplication Strong embedding similarity across different tasks (CKA) I’m opening this project for collaboration and contributions: Scaling experiments (d_model = 128 / 256) Extending to more algebraic structures Interpretability improvements Reproduction an
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