1 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
Pretraining Recurrent Networks without Recurrence
Akarsh Kumar, Phillip Isola
Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations. Standard backpropagation through time (BPTT) addresses this problem poorl…
Vector Policy Optimization: Training for Diversity Improves Test-Time Search
Ryan Bahlous-Boldi, Isha Puri, Idan Shenfeld +6
Language models must now generalize out of the box to novel environments and work inside inference-scaling search procedures, such as AlphaEvolve, that select rollouts with a varie…
Training Language Models via Neural Cellular Automata
Dan Lee, Seungwook Han, Akarsh Kumar +1
Pre-training is crucial for large language models (LLMs), as it is when most representations and capabilities are acquired. However, natural language pre-training has problems: hig…
Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
Ryan Sullivan, Akarsh Kumar, Shengyi Huang +2
Most reinforcement learning methods rely heavily on dense, well-normalized environment rewards. DreamerV3 recently introduced a model-based method with a number of tricks that miti…