  Using learned reward functions (LRFs) as a means to solve sparse-reward
reinforcement learning (RL) tasks has yielded some steady progress in
task-complexity through the years. In this work, we question whether today's
LRFs are best-suited as a direct replacement for task rewards. Instead, we
propose leveraging the capabilities of LRFs as a pretraining signal for RL.
Concretely, we propose $\textbf{LA}$nguage Reward $\textbf{M}$odulated
$\textbf{P}$retraining (LAMP) which leverages the zero-shot capabilities of
Vision-Language Models (VLMs) as a $\textit{pretraining}$ utility for RL as
opposed to a downstream task reward. LAMP uses a frozen, pretrained VLM to
scalably generate noisy, albeit shaped exploration rewards by computing the
contrastive alignment between a highly diverse collection of language
instructions and the image observations of an agent in its pretraining
environment. LAMP optimizes these rewards in conjunction with standard
novelty-seeking exploration rewards with reinforcement learning to acquire a
language-conditioned, pretrained policy. Our VLM pretraining approach, which is
a departure from previous attempts to use LRFs, can warmstart sample-efficient
learning on robot manipulation tasks in RLBench.
