Watch an AI agent learn how to balance a stick—completely from scratch—using reinforcement learning! This project walks you through how an algorithm interacts with an environment, learns through trial ...

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

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

In a groundbreaking study from 1961, Albert Bandura demonstrated that we learn by watching what others do. New evidence links ...

The Parallel-R1 framework uses reinforcement learning to teach models how to explore multiple reasoning paths at once, leading to more robust and accurate problem-solving.

Abstract: This paper investigates reinforcement learning algorithms for discrete-time stochastic multi-agent graphical games with multiplicative noise. The Bellman optimality equation for stochastic ...

DeepSeek-R1 uses reinforcement learning to teach reasoning, showing potential for AI to develop intelligence without human examples.

VLAC is a general-purpose pair-wise critic and manipulation model which designed for real world robot reinforcement learning and data refinement. It provides robust evaluation capabilities for task ...

Abstract: Reinforcement Learning (RL) serves as a fundamental learning paradigm in the field of artificial intelligence, enabling decision-making policies through interactions with environments.

We propose TraceRL, a trajectory-aware reinforcement learning method for diffusion language models, which demonstrates the best performance among RL approaches for DLMs. We also introduce a ...