A new research paper explores a phenomenon termed "post-grokking collapse" in transformers trained with the Muon optimizer. While Muon initially demonstrates faster learning on modular addition tasks, the models eventually lose generalization capabilities. This failure occurs at the interface between the model's internal representations and its output layer, with the Muon optimizer and AdamW embeddings behaving differently under specific conditions. The research suggests that the task-aligned components of the model can recover performance when isolated, indicating that the collapse is not due to fundamental representation degradation but rather an issue at the representation-readout interface. AI
IMPACT Investigates potential failure modes in transformer training, impacting future model development and optimization strategies.
RANK_REASON Research paper detailing a novel phenomenon in transformer training. [lever_c_demoted from research: ic=1 ai=1.0]
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