Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
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.
Reinforcement Learning does NOT make the base model more intelligent and limits the world of the base model in exchange for early pass performances. Graphs show that after pass 1000 the reasoning ...
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% ...
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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 ...
Amazon Web Services Inc. wants to solve the efficiency challenges of artificial intelligence agents and reduce their overall inference demands, and it’s tackling the problem with more advanced model ...
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 ...
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 Rho-alpha model incorporates sensor modalities such as tactile feedback and is trained with human guidance, says ...
Machine learning technique teaches power-generating kites to extract energy from turbulent airflows more effectively, ...
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