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 ...

Abstract: Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations. It focuses on bridging the gap between RL theory ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.

How can a small model learn to solve tasks it currently fails at, without rote imitation or relying on a correct rollout? A team of researchers from Google Cloud AI Research and UCLA have released a ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...

The age of truly autonomous artificial intelligence, where systems proactively learn, adapt and optimize amid real-world complexities instead of simply reacting, has been a long-held aspiration. Now, ...

The authors present a biologically plausible framework for action selection and learning in the striatum that is a fundamental advance in our understanding of possible neural implementations of ...