# Introduction Finding rare but useful solutions in very large candidate spaces is a recurring practical challenge across language generation, planning, and reinforcement learning. A new approach for more effective results. ## ICFA Design The new algorithm, called Inverted Causality Focusing Algorithm (ICFA), takes an innovative approach to finding solutions. It reuses an existing proposal sampler and a task-specific similarity function to create a focused sampling distribution. ## ICFA Characteristics The ICFA controls the focusing strength to avoid degeneracy. It offers a stable diagnostic based on effective sample size and explains when ICFA can reduce sample needs. ## Replicable Experiments Researchers have performed two replicable experiments: constrained language generation and sparse-reward navigation. The experiment demonstrated the effectiveness of the algorithm. ## Hybrid Architecture The algorithm has been combined with guided prompting to create a new complex architecture that combines guided inference with algorithmic reweighting.