Ben Dickson

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new paper by researchers from Google Research and the University of California, Berkeley, demonstrates that a surprisingly simple test-time scaling approach can boost the reasoning abilities of large language models (LLMs). The key? Scaling up sampling-based search, a technique that relies on generating multiple responses and using the model itself to verify them.  The core

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Reasoning through chain-of-thought (CoT) — the process by which models break problems into manageable “thoughts” before deducting answers — has become an integral part of the latest generation of frontier large language models (LLMs). However, the inference costs of reasoning models can quickly stack up as models generate excess CoT tokens. In a new paper, researchers

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The release of OpenAI GPT-4.5 has been somewhat disappointing, with many pointing out its insane price point (about 10 to 20X more expensive than Claude 3.7 Sonnet and 15 to 30X more costly than GPT-4o). However, given that this is OpenAI’s largest and most powerful non-reasoning model, it is worth considering its strengths and the areas