Enhancing GPT-Based Planning Policies by Model-Based Plan Validation

Despite Large Language Models (LLMs) have revolutionised Natural Language Processing (NLP), their capability of performing logical reasoning and automated planning is still debated. In this context, the state of the art is \model, a GPT-2 model specifically trained for planning tasks. This recent approach provides GPT-based planning policies with remarkable performance, but it can generate invalid plans containing violated action preconditions or unsatisfied goals. To address this limitation, we propose an extension of \model that integrates a plan validator into the generation process. The validator is exploited to prune invalid plan prefixes during the GPT token generation, obtaining a more robust and powerful solution to planning via GPT. We empirically evaluate the effectiveness of our approach and demonstrate its potential in various planning domains.