Sample efficiency is a measure of how much task performance a learning algorithm achieves per unit of training data or environment interaction it consumes. In reinforcement learning it is particularly critical because real-world or simulated interactions can be expensive or slow to collect, making algorithms that learn from fewer trials more practical to deploy. Techniques such as curriculum learning, model-based planning, and off-policy replay are used specifically to improve sample efficiency.