Models
FunSearch: Making new discoveries in mathematical sciences using Large Language Models
Research Published 14 December 2023 Authors Alhussein Fawzi and Bernardino Romera Paredes By searching for “functions” written in computer code, FunSearch made the first discoveries in open problems in mathematical sciences using LLMs Update: In December 2024, we published a report on arXiv showing how our method can be used to amplify human performance in…
Tufa Labs Introduced LADDER: A Recursive Learning Framework Enabling Large Language Models to Self-Improve without Human Intervention
Large Language Models (LLMs) benefit significantly from reinforcement learning techniques, which enable iterative improvements by learning from rewards. However, training these models efficiently remains challenging, as they often require extensive datasets and human supervision to enhance their capabilities. Developing methods that allow LLMs to self-improve autonomously without additional human input or large-scale architectural modifications has…
Like human brains, large language models reason about diverse data in a general way
While early language models could only process text, contemporary large language models now perform highly diverse tasks on different types of data. For instance, LLMs can understand many languages, generate computer code, solve math problems, or answer questions about images and audio. MIT researchers probed the inner workings of LLMs to better understand how they…
This AI Paper from Menlo Research Introduces AlphaMaze: A Two-Stage Training Framework for Enhancing Spatial Reasoning in Large Language Models
Artificial intelligence continues to advance in natural language processing but still faces challenges in spatial reasoning tasks. Visual-spatial reasoning is fundamental for robotics, autonomous navigation, and interactive problem-solving applications. AI systems must effectively interpret structured environments and execute sequential decisions to function in these domains. While traditional maze-solving algorithms, such as depth-first search and A*,…
Together AI’s $305M bet: Reasoning models like DeepSeek-R1 are increasing, not decreasing, GPU demand
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More When DeepSeek-R1 first emerged, the prevailing fear that shook the industry was that advanced reasoning could be achieved with less infrastructure. As it turns out, that’s not necessarily the case. At least, according to Together AI,…
Google DeepMind Releases PaliGemma 2 Mix: New Instruction Vision Language Models Fine-Tuned on a Mix of Vision Language Tasks
Vision‐language models (VLMs) have long promised to bridge the gap between image understanding and natural language processing. Yet, practical challenges persist. Traditional VLMs often struggle with variability in image resolution, contextual nuance, and the sheer complexity of converting visual data into accurate textual descriptions. For instance, models may generate concise captions for simple images but…
Open Source AI Models: Big Risks for Malicious Code, Vulns
Attackers are finding more and more ways to post malicious projects to Hugging Face and other repositories for open source artificial intelligence (AI) models, while dodging the sites’ security checks. The escalating problem underscores the need for companies pursuing internal AI projects to have robust mechanisms to detect security flaws and malicious code within their…
This AI Paper from UC Berkeley Introduces a Data-Efficient Approach to Long Chain-of-Thought Reasoning for Large Language Models
Large language models (LLMs) process extensive datasets to generate coherent outputs, focusing on refining chain-of-thought (CoT) reasoning. This methodology enables models to break down intricate problems into sequential steps, closely emulating human-like logical reasoning. Generating structured reasoning responses has been a major challenge, often requiring extensive computational resources and large-scale datasets to achieve optimal performance….
This AI Paper Explores Long Chain-of-Thought Reasoning: Enhancing Large Language Models with Reinforcement Learning and Supervised Fine-Tuning
Large language models (LLMs) have demonstrated proficiency in solving complex problems across mathematics, scientific research, and software engineering. Chain-of-thought (CoT) prompting is pivotal in guiding models through intermediate reasoning steps before reaching conclusions. Reinforcement learning (RL) is another essential component that enables structured reasoning, allowing models to recognize and correct errors efficiently. Despite these advancements,…
Gemma Scope: helping the safety community shed light on the inner workings of language models
Technologies Published 31 July 2024 Authors Language Model Interpretability team Announcing a comprehensive, open suite of sparse autoencoders for language model interpretability. To create an artificial intelligence (AI) language model, researchers build a system that learns from vast amounts of data without human guidance. As a result, the inner workings of language models are often…