arXiv Machine Learning June 25, 2026 By Samuel Valland Lyngset, Tor Viljen Raanaas, Gard Sveipe, Eirik M{\o}ller Nilsen, Jim Torresen, Kai Olav Ellefsen, Tobias L{\o}mo

Memory-Efficient Policy Libraries with Low-Rank Adaptation in Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 25700v1 Announce Type: new Abstract: 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 Adaptation (LoRA).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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