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cs.LG2026
BeNice! Closing Looping's Inference-Memory Gap via KV-Sharing
Hayden Prairie, Jacob M. Ryan, Taylor Berg-Kirkpatrick +1
Looped architectures scale quality while keeping parameter count fixed by repeatedly passing activations through the same block of layers. During inference, each recurrence generat…
cs.LG2026
Parcae: Scaling Laws For Stable Looped Language Models
Hayden Prairie, Zachary Novack, Taylor Berg-Kirkpatrick +1
Traditional fixed-depth architectures scale quality by increasing training FLOPs, typically through increased parameterization, at the expense of a higher memory footprint, or data…