NEWSLETTER
Optimization using FP4 quantization for training in the ultra -low precision language model
Large language models (LLM) have emerged as transforming tools in research and industry, with their performance directly correlating the size ...
Tag: Optimization
OPTIMIZATION OF TEST TIME PREFERENCES: A new AI framework that optimizes LLM outputs during inference with an iterative textual reward policy
Large language models (LLM) have become an indispensable part of contemporary life, shaping the future of almost all conceivable domains. ...
Microsoft AI Introduces Sigma: An Efficient Large Language Model Designed for AI Infrastructure Optimization
The advancement of artificial intelligence (ai) and machine learning (ML) has enabled transformative progress in various fields. However, the “system ...
Practical Delivery Route Optimization (TSP) with AI, using LKH and Python | by Piero Paialunga | January 2025
The code for this article can be found in this GitHub folder.ohOne of my favorite professors throughout my studies told ...
Using Optimization to Solve Adversarial Problems | by W Brett Kennedy | Jan, 2025
An example of simultaneously optimizing two policies for two adversarial agents, looking specifically at the cat and mouse game.In the ...
Exploring new dimensions of hyperparameters with approximate Bayesian Laplace optimization | by Arnaud Capitaine | January 2025
Is it better than grid search?Image from the author of canva.When I notice that my model is overfittingI often think, ...
The Prompt Alchemist: LLM-friendly automated prompt optimization for test case generation
Due to the advent of artificial intelligence (ai), the software industry has been leveraging large language models (LLMs) to complete ...
Revolutionizing LLM Alignment: A Deep Dive into Direct Q-Function Optimization
Aligning large language models (LLMs) with human preferences is an essential task in artificial intelligence research. However, current reinforcement learning ...
Hypergrid Fields: Efficient Gradient-Based Training for Scalable Neural Network Optimization
Hypernetworks have attracted attention for their ability to efficiently adapt large models or train generative models of neural representations. Despite ...
Meet OREO (Offline Reasoning Optimization) – An Offline Reinforcement Learning Method to Improve LLM Multi-Step Reasoning
Large language models (LLMs) have demonstrated impressive proficiency in numerous tasks, but their ability to perform multi-step reasoning remains a ...
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