We demonstrate that supervised learning in the transductive model enjoys such structure.

However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention.

These processes find wide application in the natural sciences and machine learning, but their inference is known to be far from trivial.

Jean-Paul Thorbjornsen, an Australian man in his mid-30s, with a rural Catholic upbringing, is a founder of THORChain, a blockchain through which users can swap one cryptocurrency for another and earn fees from making those swaps. THORChain is permissionless, so anyone can use it without getting prior approval from a centralized authority. As a decentralized network, the blockchain is built and run by operators located across the globe. During its early days, Thorbjornsen himself hid behind the pseudonym "leena" and used an AI-generated female image as his avatar. But around March 2024, he revealed his true identity as the mind behind the blockchain. If there is a central question around THORChain, it is this: Exactly who is responsible for its operations?

However, it may not be available in practice.

Continual learning (CL) empowers pre-trained vision-language (VL) models to efficiently adapt to a sequence of downstream tasks.

We show that it provides significant benefits to RL agents on a suite of gridworld, CARL and MetaWorld tasks.