Applicability of reinforcement learning to pre-trained fast weight language models

Determine whether reinforcement learning can be effectively applied to pre-trained fast weight language models that replace global attention with fixed-size memory updated online, with the goal of improving long-context modeling capabilities.

Background

Fast weight architectures such as LaCT and DeltaNet eschew global attention in favor of fixed-size memory that is updated token-by-token, enabling constant memory overhead for long contexts. Prior reinforcement learning approaches to language modeling have largely targeted standard transformer-based models, often leveraging reasoning traces and token-level rewards for next-token prediction.

The paper contrasts this prior focus with fast weight models and raises the question of whether reinforcement learning methods can be applied to these architectures. This uncertainty is explicitly noted given the architectural differences and the intended use of fast weights for on-the-fly contextual adaptation.