Developing compact yet high-performing language models remains a significant challenge in artificial intelligence. Large-scale models often require extensive computational resources, […]
Category: Machine Learning
Curiosity-Driven Reinforcement Learning from Human Feedback CD-RLHF: An AI Framework that Mitigates the Diversity Alignment Trade-off In Language Models
Large Language Models (LLMs) have become increasingly reliant on Reinforcement Learning from Human Feedback (RLHF) for fine-tuning across various applications, […]
Memorization vs. Generalization: How Supervised Fine-Tuning SFT and Reinforcement Learning RL Shape Foundation Model Learning
Modern AI systems rely heavily on post-training techniques like supervised fine-tuning (SFT) and reinforcement learning (RL) to adapt foundation models […]
The Allen Institute for AI (AI2) Releases Tülu 3 405B: Scaling Open-Weight Post-Training with Reinforcement Learning from Verifiable Rewards (RLVR) to Surpass DeepSeek V3 and GPT-4o in Key Benchmarks
Post-training techniques, such as instruction tuning and reinforcement learning from human feedback, have become essential for refining language models. But, […]
Meta AI Proposes EvalPlanner: A Preference Optimization Algorithm for Thinking-LLM-as-a-Judge
The rapid advancement of Large Language Models (LLMs) has significantly improved their ability to generate long-form responses. However, evaluating these […]
From Deep Knowledge Tracing to DKT2: A Leap Forward in Educational AI
Knowledge Tracing (KT) plays a crucial role in Intelligent Tutoring Systems (ITS) by modeling students’ knowledge states and predicting their […]
Decoupling Tokenization: How Over-Tokenized Transformers Redefine Vocabulary Scaling in Language Models
Tokenization plays a fundamental role in the performance and scalability of Large Language Models (LLMs). Despite being a critical component, […]
Microsoft now hosts AI model accused of copying OpenAI data
Fresh on the heels of a controversy in which ChatGPT-maker OpenAI accused the Chinese company behind DeepSeek R1 of using […]
Quantization Space Utilization Rate (QSUR): A Novel Post-Training Quantization Method Designed to Enhance the Efficiency of Large Language Models (LLMs)
Post-training quantization (PTQ) focuses on reducing the size and improving the speed of large language models (LLMs) to make them […]
Creating An AI Agent-Based System with LangGraph: A Beginner’s Guide
What is an Agent? An agent is a Large Language Model (LLM)-powered system that can decide its own workflow. Unlike […]
