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 ...
Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. In today’s column, I will identify and discuss an important AI ...
Google researchers introduce ‘Internal RL,’ a technique that steers an models' hidden activations to solve long-horizon tasks ...
“We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT ...
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New look at dopamine signaling suggests neuroscientists' model of reinforcement learning may need to be revised
Dopamine is a powerful signal in the brain, influencing our moods, motivations, movements, and more. The neurotransmitter is crucial for reward-based learning, a function that may be disrupted in 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 ...
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 ...
Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...
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