60 citations · 104 across the 15 of their papers we have counts for
3 papers · 1 filter
Memory-Efficient Policy Libraries with Low-Rank Adaptation in Reinforcement Learning
Samuel Valland Lyngset, Tor Viljen Raanaas, Gard Sveipe +4
When fine-tuning Large Language Models (LLMs), there has been success in minimizing both memory usage and computation with Parameter-Efficient Fine-Tuning (PEFT), like Low Rank Ada…
Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks
Kai Olav Ellefsen, Jim Torresen
A long-standing challenge in Reinforcement Learning is enabling agents to learn a model of their environment which can be transferred to solve other problems in a world with the sa…
How do Mixture Density RNNs Predict the Future?
Kai Olav Ellefsen, Charles Patrick Martin, Jim Torresen
Gaining a better understanding of how and what machine learning systems learn is important to increase confidence in their decisions and catalyze further research. In this paper, w…