Imagine trying to teach a child how to solve a tricky math problem. You might start by showing them examples, guiding them step by step, and encouraging them to think critically about their approach.
DeepSeek-R1's release last Monday has sent shockwaves through the AI community, disrupting assumptions about what’s required to achieve cutting-edge AI performance. Matching OpenAI’s o1 at just 3%-5% ...
Discover Experiential Reinforcement Learning (ERL), a revolutionary AI training paradigm that allows language models to learn from their own reflections, turning failure into structured wisdom without ...
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Leadership Amid Uncertainty: CEOs Can Learn Effective Decision Making From Reinforcement Learning
Let’s look at how RL agents are trained to deal with ambiguity, and it may provide a blueprint of leadership lessons to ...
Researchers from Fudan University and Shanghai AI Laboratory have conducted an in-depth analysis of OpenAI’s o1 and o3 models, shedding light on their advanced reasoning capabilities. These models, ...
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How the Brain Uses Reinforcement Learning Beyond Just Mean Rewards
What if our brains learned from rewards not just by averaging them but by considering their full range of possibilities? A ...
This work presents an AI-based world model framework that simulates atomic-level reconstructions in catalyst surfaces under dynamic conditions. Focusing on AgPd nanoalloys, it leverages Dreamer-style ...
The architecture of FOCUS. Given offline data, FOCUS learns a $p$ value matrix by KCI test and then gets the causal structure by choosing a $p$ threshold. After ...
A new study reveals that the next generation of blockchain defenses will not rely on fixed rules alone but on adaptive, learning-based systems capable of evolving alongside intelligent adversaries.
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