A new paper by Richard Sutton and collaborators from the University of Alberta and Openmind Institute addresses the "streaming barrier" in reinforcement learning. The research, titled "Intentional Updates for Streaming Reinforcement Learning," suggests that the root cause of the barrier is not insufficient data but incorrectly chosen step size units. The team proposes a novel approach where the step size is determined by the desired change in function output, rather than parameter movement, enhancing stability in learning processes. The paper introduces the "Intentional Updates" method, which specifies the intended outcome of each update, allowing for more precise control over learning. This approach has shown promising results, achieving performance parity with state-of-the-art algorithms like SAC on continuous control tasks without relying on large batch replay buffers. The research highlights the potential of streaming reinforcement learning to offer a more adaptive and cost-effective learning paradigm, particularly for applications with limited computational resources.