Reinforcement Learning

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NyRAG: Building Production-Ready RAG Applications with Zero Code

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, RAG, Reinforcement Learning

Retrieval-Augmented Generation (RAG) technology almost immediately became the standard in intelligent applications. This was a result of the quickly developing field of artificial intelligence that combined large language models and external knowledge bases with different real-time access methods. RAG implementation of the traditional kind poses major difficulties: complex vector database setups, intricate embedding pathways, orchestration […]

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From cloud to factory – humanoid robots coming to workplaces

ai, AI (Artificial Intelligence), Artificial Intelligence, cloud, Computer vision, distribution, manufacturing, Microsoft, Multimodal AI, physical ai, Reinforcement Learning, Robotics, Workforce & HR AI

The partnership announced this week between Microsoft and Hexagon Robotics marks an inflection point in the commercialisation of humanoid, AI-powered robots for industrial environments. The two companies will combine Microsoft’s cloud and AI infrastructure with Hexagon’s expertise in robotics, sensors, and spatial intelligence to advance the deployment of physical AI systems in real-world settings. At

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Liquid AI’s LFM2-2.6B-Exp Uses Pure Reinforcement Learning RL And Dynamic Hybrid Reasoning To Tighten Small Model Behavior

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, New Releases, Reinforcement Learning, Staff, Technology, Uncategorized

Liquid AI has introduced LFM2-2.6B-Exp, an experimental checkpoint of its LFM2-2.6B language model that is trained with pure reinforcement learning on top of the existing LFM2 stack. The goal is simple, improve instruction following, knowledge tasks, and math for a small 3B class model that still targets on device and edge deployment. Where LFM2-2.6B-Exp Fits

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How We Learn Step-Level Rewards from Preferences to Solve Sparse-Reward Environments Using Online Process Reward Learning

Artificial Intelligence, Editors Pick, Reinforcement Learning, Technology, Tutorials

In this tutorial, we explore Online Process Reward Learning (OPRL) and demonstrate how we can learn dense, step-level reward signals from trajectory preferences to solve sparse-reward reinforcement learning tasks. We walk through each component, from the maze environment and reward-model network to preference generation, training loops, and evaluation, while observing how the agent gradually improves

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