These days, large language models can handle increasingly complex tasks, writing complex code and engaging in sophisticated reasoning. But when it comes to four-digit multiplication, a task taught in ...

Large reasoning models often show counterintuitive behavior, putting more computational effort into simple tasks than difficult ones while producing worse results overall. Researchers have established ...

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...

Most current benchmarks, such as GSM8K and MATH, evaluate LRMs by asking one question at a time. While effective for initial model development, this isolated question approach faces two critical ...

AI reasoning models were supposed to be the industry's next leap, promising smarter systems able to tackle more complex problems and a path to superintelligence. The latest releases from the major ...

AI reasoning models were supposed to be the industry’s next leap, promising smarter systems able to tackle more complex problems. Now, a string of research is calling that into question. Researchers ...

In early June, Apple researchers released a study suggesting that simulated reasoning (SR) models, such as OpenAI’s o1 and o3, DeepSeek-R1, and Claude 3.7 Sonnet Thinking, produce outputs consistent ...

A new wave of “reasoning” systems from companies like OpenAI is producing incorrect information more often. Even the companies don’t know why. Credit...Erik Carter Supported by By Cade Metz and Karen ...

Despite notable advancements in large language models (LLMs), effective performance on reasoning-intensive tasks—such as mathematical problem solving, algorithmic planning, or coding—remains ...

NVIDIA’s GTC 2025 conference showcased significant advancements in AI reasoning models, emphasizing progress in token inference and agentic capabilities. A central highlight was the unveiling of the ...