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 ...

Which Machine Learning Models Are Most Used In Crypto Signal Generation?

Machine learning is transforming how crypto traders create and understand signals. From supervised models such as Random Forests and Gradient Boosting Machines to sophisticated deep learning hybrids ...
eLife

Critique of impure reason: Unveiling the reasoning behaviour of medical large language models

A survey of reasoning behaviour in medical large language models uncovers emerging trends, highlights open challenges, and introduces theoretical frameworks that enhance reasoning behaviour ...

Berkeley boffins build better load balancing algo with AI

The UC Berkeley crew has now shown the value of AI-based optimization work by having OpenEvolve work out a more efficient approach to load balancing across GPUs handling LLM inference.
IEEE

Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications

Abstract: Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. Most of the existing ...
TechCrunch

The reinforcement gap — or why some AI skills improve faster than others

AI coding tools are getting better fast. If you don’t work in code, it can be hard to notice how much things are changing, but GPT-5 and Gemini 2.5 have made a whole new set of developer tricks ...
GeekWire

CoreWeave to acquire OpenPipe, a Seattle-area startup that uses reinforcement learning to ...

GeekWire chronicles the Pacific Northwest startup scene. Sign up for our weekly startup newsletter, and check out the GeekWire funding tracker and VC directory. by Taylor Soper on Sep 4, 2025 at 8:00 ...
Scientific Research Publishing

Ribba, B. (2023) Reinforcement Learning as an Innovative Model-Based Approach: Examples ...

ABSTRACT: Depression treatment often involves a complex and lengthy trial-and-error process, where clinicians sequentially prescribe medications to identify the most ...
Law

Training Alone Is Not Enough: Lessons From 'Recentive' and USPTO AI Examples on Patent ...

Artificial intelligence (AI) has become a foundational technology across countless industries, but the question of when machine-learning (ML) inventions are patent-eligible remains a nebulous target.
marktechpost

DSRL: A Latent-Space Reinforcement Learning Approach to Adapt Diffusion Policies in Real ...

Robotic control systems have made significant progress through methods that replace hand-coded instructions with data-driven learning. Instead of relying on explicit programming, modern robots learn ...
GitHub

Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Our training pipeline is adapted from verl and rllm(DeepScaleR). The installation commands that we verified as viable are as follows: conda create -y -n rlvr_train ...