Self-improving language models are becoming reality with MIT's updated SEAL technique

Researchers at the Massachusetts Institute of Technology (MIT) are gaining renewed attention for developing and open sourcing ...
Tech Xplore on MSN

DeepMind introduces AI agent that learns to complete various tasks in a scalable world model

Over the past decade, deep learning has transformed how artificial intelligence (AI) agents perceive and act in digital ...

This Startup Wants to Spark a US DeepSeek Moment

With the US falling behind on open source models, one startup has a bold idea for democratizing AI: let anyone run reinforcement learning.

The next ‘golden age’ of AI investment

A16z’s Anjney Midha says reasoning models and new frontier teams will spark the next big wave in AI investment.
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Breaking the spurious link: How causal models fix offline reinforcement learning's generalization problem

Researchers from Nanjing University and Carnegie Mellon University have introduced an AI approach that improves how machines learn from past data—a process known as offline reinforcement learning.

Nvidia researchers boost LLMs reasoning skills by getting them to 'think' during pre-training

By teaching models to reason during foundational training, the verifier-free method aims to reduce logical errors and boost ...

AI Agents, LLMs & Economic Growth : Karpathy’s Surprising Predictions

Discover Andrej Karpathy's insights on AI agents, LLMs, and economic growth. Insights on memory, education, and economic ...
Unite.AI

The End of Tabula Rasa: How Pre-Trained World Models are Redefining Reinforcement Learning

For a long time, the core idea in reinforcement learning (RL) was that AI agents should learn every new task from scratch, like a blank slate. This "tabula rasa" approach led to amazing achievements, ...
AZoLifeSciences on MSN

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 ...
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 ...