3 accepted papers
How does the amount of compute available to a reinforcement learning (RL) policy affect its learning? Can policies using a fixed amount of parameters, still benefit from additional compute? The standard RL framework does not provide a language to answer these questions formally. Empirically, deep RL…
In classical AI, perception relies on learning state-based representations, while planning --- temporal reasoning over action sequences --- is typically achieved through search. We study whether such reasoning can instead emerge from representations that capture both perceptual and temporal structu…
Text embeddings are essential for tasks such as document retrieval, clustering, and semantic similarity assessment. In this paper, we study how to contrastively train text embedding models in a compute-optimal fashion, given a suite of pretrained decoder-only language models. Our innovation is an al…