BES combines evolutionary operators with task decomposition to escape the limitations of autoregressive-only search, enabling language models to find better solutions during both training and inference on challenging reasoning tasks.
This paper introduces Bidirectional Evolutionary Search (BES), a method that improves language models by combining forward search (generating new solutions by mixing partial trajectories) with backward search (breaking tasks into verifiable subtasks).