MIT researchers develop an efficient way to train more reliable AI agents | MIT News
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Fields ranging from robotics to medicine to political science are attempting to train AI systems to make meaningful decisions of all kinds….
Meet Arch 0.1.3: Open-Source Intelligent Proxy for AI Agents
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The integration of AI agents into various workflows has increased the need for intelligent coordination, data routing, and enhanced security among systems….
The Allen Institute for AI (AI2) Releases Tülu 3: A Set of State-of-the-Art Instruct Models with Fully Open Data, Eval Code, and Training Algorithms
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The Allen Institute for AI (AI2) has announced the release of Tülu 3, a state-of-the-art family of instruction-following models designed to set…
Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum
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Large language models (LLMs) are commonly trained on datasets consisting of fixed-length token sequences. These datasets are created by randomly concatenating documents…
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
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Learning with identical train and test distributions has been extensively investigated both practically and theoretically. Much remains to be understood, however, in…
Microsoft Research Introduces Reducio-DiT: Enhancing Video Generation Efficiency with Advanced Compression
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Recent advancements in video generation models have enabled the production of high-quality, realistic video clips. However, these models face challenges in scaling…
Faster Algorithms for User-Level Private Stochastic Convex Optimization
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We study private stochastic convex optimization (SCO) under user-level differential privacy (DP) constraints. In this setting, there are nnn users, each possessing…
Black Forest Labs Release FLUX.1 Tools: A Suite of AI Models Designed to Add Control and Steerability to the Base Text-to-Image Model FLUX.1
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In a world where visual content is increasingly essential, the ability to create and manipulate images with precision and creativity is invaluable….
Do Compressed LLMs Forget Knowledge? An Experimental Study with Practical Implications
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This paper was accepted at the Machine Learning and Compression Workshop at NeurIPS 2024. Compressing Large Language Models (LLMs) often leads to…
Instance-Optimal Private Density Estimation in the Wasserstein Distance
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Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is…