Last updated: October 11, 2025 by the editorial team
Author's): MKSave here
Originally published in Towards Artificial Intelligence.
New training method teaches language models to generate reasoning strategies first, improving accuracy by 44% on complex math problems
Large language models suffer from a specific problem: they optimize to generate longer solutions, rather than exploring different problem-solving strategies. Scientists call this “reasoning.”
In this article, the authors discuss the limitations of large language models in problem solving, in particular their tendency to favor long-term solutions over strategic exploration. Introducing RLAD (reinforcement learning for abstraction discovery), they describe its effectiveness in training AI systems to first generate high-level reasoning strategies, resulting in a noticeable 44% performance increase on math tests. The paper also examines the fundamental principles of abstract reasoning, the dual training process associated with RLAD, and its implications for enhancing the metacognitive capabilities of artificial intelligence in various domains.
Read the entire blog for free on Medium.
Published via Towards AI
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