marktechpost3d
Google DeepMind Achieves State-of-the-Art Data-Efficient Reinforcement Learning RL with Improved Transformer World Models
Reinforcement Learning RL trains agents to maximize rewards by interacting with an environment. Online RL alternates between taking actions, collecting observations and rewards, and updating policies ...
marktechpost4d
Meta AI Introduces VideoJAM: A Novel AI Framework that Enhances Motion Coherence in AI-Generated Videos
Despite recent advancements, generative video models still struggle to represent motion realistically. Many existing models focus primarily on pixel-level reconstruction, often leading to ...
marktechpost4d
ByteDance Proposes OmniHuman-1: An End-to-End Multimodality Framework Generating Human Videos based on a Single Human Image and Motion Signals
Despite progress in AI-driven human animation, existing models often face limitations in motion realism, adaptability, and scalability. Many models struggle to generate fluid body movements and rely ...
marktechpost5d
Top AI Coding Agents in 2025
AI-powered coding agents have significantly transformed software development in 2025, offering advanced features that enhance productivity and streamline workflows. Below is an overview of some of the ...
marktechpost4d
Zep AI Introduces a Smarter Memory Layer for AI Agents Outperforming the MemGPT in the Deep Memory Retrieval (DMR) Benchmark
The development of transformer-based large language models (LLMs) has significantly advanced AI-driven applications, particularly conversational agents. However, these models face inherent limitations ...
marktechpost7d
This AI Paper from the Tsinghua University Propose T1 to Scale Reinforcement Learning by Encouraging Exploration and Understand Inference Scaling
Large language models (LLMs) are developed specifically for math, programming, and general autonomous agents and require improvement in reasoning at test time. Various approaches include producing ...
marktechpost4d
NYU Researchers Introduce WILDCHAT-50M: A Large-Scale Synthetic Dataset for Efficient LLM Post-Training
Large language model (LLM) post-training focuses on refining model behavior and enhancing capabilities beyond their initial training phase. It includes supervised fine-tuning (SFT) and reinforcement ...
marktechpost4d
Fine-Tuning Llama 3.2 3B Instruct for Python Code: A Comprehensive Guide with Unsloth
In this tutorial, we’ll walk through how to set up and perform fine-tuning on the Llama 3.2 3B Instruct model using a specially curated Python code dataset. By the end of this guide, you’ll have a ...
marktechpost5d
University of Bath Researchers Developed an Efficient and Stable Machine Learning Training Method for Neural ODEs with O(1) Memory Footprint
Neural Ordinary Differential Equations are significant in scientific modeling and time-series analysis where data changes every other moment. This neural network-inspired framework models ...
marktechpost10d
Creating An AI Agent-Based System with LangGraph: A Beginner’s Guide
For example, when a user asks a question, the LLM analyzes the input and decides whether it can answer directly or if additional steps (like a web search) are needed.
marktechpost6d
Transformer-Based Modulation Recognition: A New Defense Against Adversarial Attacks
The fast development of wireless communication technologies has increased the application of automatic modulation recognition (AMR) in sectors such as cognitive radio and electronic countermeasures.
marktechpost4d
Creating an AI Agent-Based System with LangGraph: Putting a Human in the Loop
In our previous tutorial, we built an AI agent capable of answering queries by surfing the web and added persistence to maintain state. However, in many scenarios, you may want to put a human in the ...