89 citations · 133 across the 14 of their papers we have counts for
3 papers · 1 filter
Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
Seijin Kobayashi, Yanick Schimpf, Maximilian Schlegel +12
Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented success on many problem domains. Du…
Do Depth-Grown Models Overcome the Curse of Depth? An In-Depth Analysis
Ferdinand Kapl, Emmanouil Angelis, Tobias Höppe +4
Gradually growing the depth of Transformers during training can not only reduce training cost but also lead to improved reasoning performance, as shown by MIDAS (Saunshi et al., 20…
MesaNet: Sequence Modeling by Locally Optimal Test-Time Training
Johannes von Oswald, Nino Scherrer, Seijin Kobayashi +14
Sequence modeling is currently dominated by causal transformer architectures that use softmax self-attention. Although widely adopted, transformers require scaling memory and compu…