We present a method for using adverb phrases to adjust skill parameters via learned \textit{adverb-skill groundings}. These groundings allow an agent to use adverb feedback provided by a human to directly update a skill policy in a manner similar to traditional local policy search methods. We show that our method can be used as a drop-in replacement for these policy search methods when dense reward from the environment is not available but human language feedback is. We demonstrate improved sample efficiency over modern policy search methods in two experiments.