SPIRAL: Learning to Search and Aggregate

The SPIRAL framework introduces a novel approach to language model reasoning by integrating Sequential-Parallel-Aggregative Reinforcement Learning, allowing models to utilize sequential reasoning, independently sampled parallel traces, and aggregation in a unified inference pipeline. This method demonstrates significant improvements in scaling inference compute, achieving up to 11 times greater scaling efficiency and 15% enhanced performance on reasoning tasks compared to the GRPO baseline. Practitioners can leverage SPIRAL to optimize their models for more efficient and effective reasoning capabilities in AI applications.