newsReddit r/MachineLearningTrust 52 · CommunityPublished 4d agoLive · 4d ago
A trained fast-weight memory: a 3M-param transformer installs never-trained rules at inference, forward-only — where test-time training transfers nothing (single RTX 3090, fully reproducible) [R]
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paperThe State-Prediction Separation HypothesispaperFrom SRA to Self-Flow: Data Augmentation or Self-Supervision?paperIs One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL TrainingpaperCARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear AttentionpaperCHERRY: Compressed Hierarchical Experts with Recurrent Representational YieldpaperMemDefrag: Latent Memory Defragmentation for Large Language Models
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paperThe State-Prediction Separation HypothesispaperFrom SRA to Self-Flow: Data Augmentation or Self-Supervision?paperCHERRY: Compressed Hierarchical Experts with Recurrent Representational YieldpaperCARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear AttentionpaperSuper Weights in LLMs and the Failure of Selective TrainingpaperMemDefrag: Latent Memory Defragmentation for Large Language ModelspaperIs One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL TrainingpaperSystematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures