Latent Planning via Embedding Arithmetic: A Contrastive Approach to Strategic Reasoning

The recent paper 'Latent Planning via Embedding Arithmetic: A Contrastive Approach to Strategic Reasoning' presents SOLIS, a method that leverages supervised contrastive learning to create an evaluation-aligned embedding space for planning in complex decision environments. By capturing outcome similarities through proximity in this space, SOLIS enables a shallow search strategy in chess, achieving competitive performance under constrained conditions. This innovative approach not only demonstrates the effectiveness of SOLIS in a practical application like chess but also highlights the potential of evaluation-aligned latent planning as a lightweight alternative to traditional dynamics models or policy learning. The implications of this research extend beyond chess, suggesting new avenues for enhancing strategic reasoning in AI across various domains.