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cs.LG2026
On Training in Imagination
Nadav Timor, Ravid Shwartz-Ziv, Micah Goldblum +2
State-of-the-art model-based reinforcement learning methods train policies on imagined rollouts. These rollouts are trajectories generated by a learned dynamics model and are score…
cs.LG2025
NdLinear: Preserving Multi-Dimensional Structure for Parameter-Efficient Neural Networks
Alex Reneau, Jerry Yao-Chieh Hu, Zhongfang Zhuang +6
In deep learning, processing multidimensional inputs (e.g., images, medical scans, and time series) is an important task that often requires flattening the inputs. We introduce $\m…
cs.LG2025
Out-of-Vocabulary Sampling Boosts Speculative Decoding
Nadav Timor, Jonathan Mamou, Oren Pereg +2
Speculative decoding relies on fast and accurate drafters. Recent state-of-the-art language models employ larger and larger vocabularies, which significantly slows down drafters. O…