Instead of sampling harder or using more compute at test time, decomposing problems into independently solvable modules lets you generate exponentially more solutions while drastically cutting GPU costs—solving problems that standard generation cannot reach.
DecompRL teaches LLMs to solve hard coding problems by breaking them into smaller, reusable modules rather than just sampling more attempts. By learning to generate modular code structures, the approach creates exponentially more candidate solutions (k^n combinations from k implementations of n modules) while reducing GPU costs by ~50x, shifting computation to cheaper CPU evaluation.