NextBigFuture

Up to Date Technical Dive into State of AI

Hands-on learning is praised as the best way to understand AI internals. The conversation aims to be technical without ...
GitHub

Pioneering Perception Policy with Reinforcement Learning

We present Perception-R1, a scalable RL framework using Group Relative Policy Optimization (GRPO) during MLLM post-training. Key innovations: 🎯 Perceptual Perplexity Analysis: We introduce a novel ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
IEEE

Preference-Based Multi-Objective Reinforcement Learning

Abstract: Multi-objective reinforcement learning (MORL) is a structured approach for optimizing tasks with multiple objectives. However, it often relies on pre-defined reward functions, which can be ...
GitHub

Demystifying Reinforcement Learning in Agentic Reasoning

An overview of our research on agentic RL. In this work, we systematically investigate three dimensions of agentic RL: data, algorithms, and reasoning modes. Our findings reveal: Real end-to-end ...
IEEE

SA-MARL: Novel Self-Attention-Based Multi-Agent Reinforcement Learning With Stochastic Gradient Descent

Abstract: In the rapidly advancing Reinforcement Learning (RL) field, Multi-Agent Reinforcement Learning (MARL) has emerged as a key player in solving complex real-world challenges. A pivotal ...