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Procedural planning, which entails decomposing a high-level goal into asequence of temporally ordered steps, is an important yet intricate task formachines. It involves integrating common-sense knowledge to reason aboutcomplex contextualized situations that are often counterfactual, e.g."scheduling a doctor's appointment without a phone". While current approachesshow encouraging results using large language models (LLMs), they are hinderedby drawbacks such as costly API calls and reproducibility issues. In thispaper, we advocate planning using smaller language models. We present PlaSma, anovel two-pronged approach to endow small language models with proceduralknowledge and (counterfactual) planning capabilities. More concretely, wedevelop symbolic procedural knowledge distillation to enhance the implicitknowledge in small language models and an inference-time algorithm tofacilitate more structured and accurate reasoning. In addition, we introduce anovel task, Counterfactual Planning, that requires a revision of a plan to copewith a counterfactual situation. In both the original and counterfactualsetting, we show that orders-of-magnitude smaller models (770M-11B parameters)can compete and often surpass their larger teacher models' capabilities.

PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning