8 papers
PyLO: Towards Accessible Learned Optimizers in PyTorch
Paul Janson, Benjamin Therien, Quentin Anthony +3
Learned optimizers have been an active research topic over the past decade, with increasing progress toward practical, general-purpose optimizers that can serve as drop-in replacem…
Attention to Mamba: A Recipe for Cross-Architecture Distillation
Abhinav Moudgil, Ningyuan Huang, Eeshan Gunesh Dhekane +3
State Space Models (SSMs) such as Mamba have become a popular alternative to Transformer models, due to their reduced memory consumption and higher throughput at generation compare…
Celo2: Towards Learned Optimization Free Lunch
Abhinav Moudgil, Boris Knyazev, Eugene Belilovsky
Learned optimizers are powerful alternatives to hand-designed update rules like Adam, yet they have seen limited practical adoption since they often fail to meta-generalize beyond…
AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents
Alisia Lupidi, Bhavul Gauri, Thomas Simon Foster +34
LLM agents hold significant promise for advancing scientific research. To accelerate this progress, we introduce AIRS-Bench (the AI Research Science Benchmark), a suite of 20 tasks…
Celo: Training Versatile Learned Optimizers on a Compute Diet
Abhinav Moudgil, Boris Knyazev, Guillaume Lajoie +1
Learned optimization has emerged as a promising alternative to hand-crafted optimizers, with the potential to discover stronger learned update rules that enable faster, hyperparame…
Understanding Input Selectivity in Mamba: Impact on Approximation Power, Memorization, and Associative Recall Capacity
Ningyuan Huang, Miguel Sarabia, Abhinav Moudgil +3
State-Space Models (SSMs), and particularly Mamba, have recently emerged as a promising alternative to Transformers. Mamba introduces input selectivity to its SSM layer (S6) and in…