Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)

Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...

How Google’s 'internal RL' could unlock long-horizon AI agents

Google researchers introduce ‘Internal RL,’ a technique that steers an models' hidden activations to solve long-horizon tasks ...
NextBigFuture

Reinforcement Learning Does NOT Fundamentally Improve AI Models

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 ...
Analytics India Magazine

Complex Reinforcement Learning Tasks Can Cost Up to $20,000 Each: EpochAI Report

Among those interviewed, one RL environment founder said, “I’ve seen $200 to $2,000 mostly. $20k per task would be rare but ...
SiliconANGLE

AWS simplifies AI agent customization with automated reinforcement learning

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 ...
Physics World

Reinforcement learning could help airborne wind energy take off

Machine learning technique teaches power-generating kites to extract energy from turbulent airflows more effectively, ...
EurekAlert!

Reinforcement learning world models for catalyst surface reconstruction: state-of-the-art review

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 ...
Interesting Engineering on MSN

AI-trained quadruped robot walks rough, low-friction terrain without human input

This multi-objective setup encourages natural walking behavior rather than rigid or inefficient movement. A four-stage ...
Devdiscourse

AI trading systems mimicking human bias show higher risk

Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...