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Fine-Tuning Custom Embedding Models for Enhanced Retrieval Performance
The source outlines the process and benefits of fine-tuning custom embedding models, particularly for improving Retrieval-Augmented Generation (RAG) …
1Â year, 2Â months ago
AdLlama: Boosting Ad CTR with Reinforcement Learning
This text describes research by Meta Platforms on improving generative AI for Facebook ad text, specifically through a new method called Reinforcemen…
1Â year, 2Â months ago
Machine Learning: Models, Algorithms, and Reinforcement Learning
This source offers an extensive overview of machine learning concepts, beginning with supervised learning methods like linear regression, logistic re…
1Â year, 2Â months ago
Mixture-of-Recursions: Adaptive Computation for Language Models
The provided source introduces Mixture-of-Recursions (MoR), a novel Transformer architecture designed to enhance the efficiency of large language mod…
1Â year, 2Â months ago
Operator-Based Machine Intelligence: A Hilbert Space Framework
This document presents an alternative paradigm for machine learning, shifting from traditional neural networks to a framework rooted in infinite-dime…
1Â year, 2Â months ago
Meta CLIP 2: A Worldwide Scaling Recipe
The academic paper introduces Meta CLIP 2, a novel approach to training Contrastive Language-Image Pretraining (CLIP) models using a vast, worldwide …
1Â year, 2Â months ago
In-Context Learning: Implicit Weight Dynamics
This academic paper explores In-Context Learning (ICL) in Large Language Models (LLMs), a phenomenon where models learn new patterns from prompts wit…
1Â year, 2Â months ago
GLM-4.5: Open Agentic, Reasoning, and Coding Foundation Models
The source introduces GLM-4.5, a new open-source Mixture-of-Experts (MoE) large language model, along with a compact version, GLM-4.5-Air. Developed …
1Â year, 2Â months ago
RLVMR: Verifiable Meta-Reasoning for Long-Horizon Agents
The document introduces RLVMR (Reinforcement Learning with Verifiable Meta-Reasoning Rewards), a novel framework designed to enhance the performance …
1Â year, 2Â months ago
CoT-Self-Instruct: High-Quality Synthetic Prompt Generation
The research introduces CoT-Self-Instruct, a novel method for generating high-quality synthetic data to train Large Language Models (LLMs). This appr…
1Â year, 2Â months ago