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Large Language Models (LLMs) and Their Reasoning Capabilities LLMs can solve math problems, make logical inferences, and assist in programming. Their success often depends on two methods: supervised fine-tuning (SFT) with human help and inference-time search with external checks. While SFT provides structured reasoning, it demands a lot of human effort and is limited by…

Understanding Reinforcement Learning (RL) Reinforcement Learning (RL) helps agents learn how to maximize rewards by interacting with their environment. There are two main types: Online RL: This method involves taking actions, observing results, and updating strategies based on experiences. Model-free RL (MFRL): This approach connects observations to actions but needs a lot of data. Model-based…

Challenges with Generative Video Models Generative video models have made progress, yet they still face issues accurately depicting motion. Many current models prioritize pixel accuracy, which can lead to problems such as: Unrealistic physics Missing frames Distortions in complex movements This is particularly evident in dynamic actions such as gymnastics and object interactions. Improving these…

Creating an AI Agent with Human Oversight Introduction In this tutorial, we will enhance our AI agent by adding a human oversight feature. This allows a person to monitor and approve the agent’s actions using LangGraph. Let’s see how we can implement this. Setting Up the Agent We will start from our previous setup. First,…

Introducing Crossfire: A New Defense for Graph Neural Networks What are Graph Neural Networks (GNNs)? Graph Neural Networks (GNNs) are used in many areas like natural language processing, social networks, and recommendation systems. However, protecting GNNs from attacks is a major challenge. The Challenge of Bit Flip Attacks (BFAs) Bit Flip Attacks manipulate bits in…

Challenges in Current AI Animation Models Current AI models for human animation face several issues, including: Motion Realism: Many struggle to create realistic and fluid body movements. Adaptability: Existing models often rely on limited training datasets, making them less flexible. Facial vs. Full-Body Animation: While facial animation has improved, full-body animation remains inconsistent. Aspect Ratio…

Fine-Tuning Llama 3.2 3B Instruct for Python Code Overview In this guide, we’ll show you how to fine-tune the Llama 3.2 3B Instruct model using a curated Python code dataset. By the end, you will understand how to customize large language models for coding tasks and gain practical insights into the tools and configurations required…

Challenges in Vision-Language Models (VLMs) Vision-language models (VLMs) struggle to generalize well beyond their training data while keeping costs low. Techniques like chain-of-thought supervised fine-tuning (CoT-SFT) often lead to overfitting, where models excel on familiar data but fail with new scenarios. This limits their usefulness in fields like autonomous systems, medical imaging, and visual reasoning.…

Post-Training for Large Language Models (LLMs) Understanding Post-Training: Post-training enhances LLMs by fine-tuning their performance beyond initial training. This involves techniques like supervised fine-tuning (SFT) and reinforcement learning to meet human needs and specific tasks. The Role of Synthetic Data Synthetic data is vital for improving LLMs, helping researchers evaluate and refine post-training methods. However,…

Transforming AI Memory with Zep Introduction to Zep Zep is a new memory layer for AI agents that improves how they remember and retrieve information. It addresses the limitations of traditional AI models, which often lose track of important details over time. Key Benefits of Zep – **Enhanced Memory Retention**: Zep uses a dynamic knowledge…