RLAD (Reinforcement Learning with Abstraction and Deduction) trains models via RL using a 2-player setup: ▪️ An abstraction generator – proposes short, natural-language “reasoning hints” (abstractions) summarizing key facts and strategies. ▪️ A solution generator – uses them to solve problems. This method separates "how to reason" from "how to answer," achieving ~44% improvement over standard long-chain reasoning methods. Here's how it works: