RLAD: How AI Learns to Think Strategically Before Solving Hard Problems
Last Updated on October 11, 2025 by Editorial Team
Author(s): MKWriteshere
Originally published on Towards AI.
A new training method teaches language models to generate reasoning strategies first, improving accuracy by 44% on complex math problems
Large language models struggle with a specific problem: they optimize for generating longer solutions instead of exploring different problem-solving strategies. Researchers call this “underthinking.”
In the article, the authors discuss the limitations of large language models in problem-solving, particularly their tendency to prioritize lengthy solutions over strategic exploration. Introducing RLAD (Reinforcement Learning to discover Abstractions), they describe its effectiveness in teaching AI systems to first generate high-level reasoning strategies, resulting in a notable performance boost of 44% on mathematical benchmarks. The paper also explores the underlying principles of reasoning abstractions, the dual training process involved in RLAD, and its implications for enhancing AI’s metacognitive capabilities across various domains.
Read the full blog for free on Medium.
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