A reinforcement learning environment is a fail-safe digital practice room where an agent can afford to make mistakes and learn from them without real-world consequences.

Anthropic research shows developers using AI assistance scored 17% lower on comprehension tests when learning new coding ...

New benchmark shows top LLMs achieve only 29% pass rate on OpenTelemetry instrumentation, exposing the gap between ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Dive into Faraday’s Law of Electromagnetic Induction with a practical Python implementation in this first part of our Electrodynamics series. Learn how to simulate and visualize changing magnetic ...

Abstract: In 2019, the JPEG Standardization Committee initiated JPEG AI to define the first image coding specifications, taking advantage of an end-to-end learning-based coding approach. The JPEG AI ...

CNBC put the AI threat to software companies to the test by vibe-coding a version of the tools from Monday.com. Silicon Valley insiders say the most exposed software names are the ones that "sit on ...

verl is a flexible, efficient and production-ready RL training library for large language models (LLMs). verl is the open-source version of HybridFlow: A Flexible and Efficient RLHF Framework paper.

Recently, there have been significant research interests in training large language models (LLMs) with reinforcement learning (RL) on real-world tasks, such as multi-turn code generation. While online ...

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...

Abstract: Recent studies in reinforcement learning have explored brain-inspired function approximators and learning algorithms to simulate brain intelligence and adapt to neuromorphic hardware. Among ...