March 2025

Hi everyone, As you know, I’ve been writing a lot lately about the latest research on reasoning in LLMs. Before my next research-focused blog post, I wanted to offer something special to my paid subscribers as a thank-you for your ongoing support. So, I’ve started writing a new book on how reasoning works in LLMs, and here I’m sharing the first Chapter 1 with you. This ~15-page chapter is an introduction reasoning in the context of LLMs and […]

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Training large language models more efficiently Training separate models on different datasets and then merging them reduces computational costs by as much as 91%. Conversational AI Dhananjay Ram Nikolaos Pappas March 27, 01:10 PM March 27, 01:10 PM Large language models (LLMs) go through several stages of training on mixed datasets with different distributions, stages that include pretraining, instruction tuning, and reinforcement learning from human feedback. Finding the optimal mix of data distributions across datasets is essential to […]

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Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle “stop-and-go” waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient flow-smoothing controllers, we built fast, data-driven simulations that RL agents interact with, learning to maximize energy efficiency while maintaining throughput and operating safely […]

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Objective EchoPrime is a foundation model designed for comprehensive echocardiographic interpretation. Unlike previous models that use single views or static images, EchoPrime integrates multi-view echocardiographic videos with corresponding text reports using a vision-language framework. Automated echocardiogram interpretation without supervised learning: EchoPrime can interpret complex cardiac features and diagnoses directly from echocardiographic video-text data, without needing supervised fine-tuning for each task. Key Features Massive dataset: Over 12 million videos From 275,442 studies Involving 108,913 patients Data come from 2 […]

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Empowering disaster preparedness: AIs role in navigating complex climate risks AI systems that integrate meteorological, geospatial, and socioeconomic data can deliver warnings that are more localized and more timely. Sustainability Markus Reichstein Boran Han March 21, 10:15 AM March 21, 10:15 AM As climate change intensifies, our ability to predict and respond to cascading and compounding disasters grows increasingly critical. Floods, droughts, wildfires, and extreme storms are no longer isolated events; they interact in ways that defy traditional […]

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Amazon Nova AI Challenge accelerating the field of generative AI Inaugural global university competition focused on advancing secure, trusted AI-assisted software development. Conversational AI Staff writer March 10, 09:11 AM March 17, 04:46 PM At Amazon, responsible AI development includes partnering with leading universities to foster breakthrough research. Recognizing that many academic institutions lack the resources for large-scale studies, we’re transforming the landscape with the Amazon Nova AI Challenge. While the Amazon Nova AI Challenge will explore various […]

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Improving the reasoning abilities of large language models (LLMs) has become one of the hottest topics in 2025, and for good reason. Stronger reasoning skills allow LLMs to tackle more complex problems, making them more capable across a wide range of tasks users care about. In the last few weeks, researchers have shared a large number of new strategies to improve reasoning, including scaling inference-time compute, reinforcement learning, supervised fine-tuning, and distillation. And many approaches combine these techniques […]

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<!– –>    Dataset    Code Recently, we introduced Platinum Benchmarks as a step toward quantifying the reliability of large language models (LLMs). In that work, we revised older benchmarks to minimize label noise, such as ambiguous or mislabeled examples, and showed that frontier LLMs still make genuine errors on simple questions. For example, as part of that work we revised a 300-problem subset of GSM8K, a dataset of grade school math word problems, and found that all […]

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