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We present ScienceWorld, a benchmark to test agents' scientific reasoningabilities in a new interactive text environment at the level of a standardelementary school science curriculum. Despite the transformer-based progressseen in question-answering and scientific text processing, we find that currentmodels cannot reason about or explain learned science concepts in novelcontexts. For instance, models can easily answer what the conductivity of aknown material is but struggle when asked how they would conduct an experimentin a grounded environment to find the conductivity of an unknown material. Thisbegs the question of whether current models are simply retrieving answers byway of seeing a large number of similar examples or if they have learned toreason about concepts in a reusable manner. We hypothesize that agents need tobe grounded in interactive environments to achieve such reasoning capabilities.Our experiments provide empirical evidence supporting this hypothesis --showing that a 1.5 million parameter agent trained interactively for 100k stepsoutperforms a 11 billion parameter model statically trained for scientificquestion-answering and reasoning from millions of expert demonstrations.

ScienceWorld: Is your Agent Smarter than a 5th Grader?