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Supervised learning made easy: Real-world example explained

In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its applications. Supervised learning is a type of Machine Learning which learns ...
The Robot Report

AgiBot deploys its Real-World Reinforcement Learning system

AgiBot announced a key milestone this week with the successful deployment of its Real-World Reinforcement Learning system in a manufacturing pilot with Longcheer Technology. The pilot project marks ...
acm.org

Shields for Safe Reinforcement Learning

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...
marktechpost

Memory-R1: How Reinforcement Learning Supercharges LLM Memory Agents

Large language models (LLMs) now stand at the center of countless AI breakthroughs—chatbots, coding assistants, question answering, creative writing, and much more. But despite their prowess, they ...
Seeking Alpha

Braze, Inc. (BRZE) Q1 2026 Earnings Call Transcript

Welcome to the Braze Fiscal First Quarter 2026 Earnings Conference Call. My name is Luke, and I'll be your operator for today's call. [Operator Instructions] I'll now turn the call over to Christopher ...
GitHub

Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Our training pipeline is adapted from verl and rllm(DeepScaleR). The installation commands that we verified as viable are as follows: conda create -y -n rlvr_train ...
Frontiers

Co-Learning: code learning for multi-agent reinforcement collaborative framework with conversational natural language interfaces

Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Faculty of Science and Technology, Beijing Normal University-Hong Kong Baptist University United ...
eLife

Dynamics of striatal action selection and reinforcement learning

The authors present a biologically plausible framework for action selection and learning in the striatum that is a fundamental advance in our understanding of possible neural implementations of ...