Dynamic programming (DP) can be inefficient if not carefully implemented, as it often involves solving overlapping subproblems. To improve the efficiency of DP solutions, first, reduce the state space by identifying and eliminating redundant subproblems. Memoization helps store already computed results, avoiding redundant recalculations. For large input sizes, consider converting recursive DP to an iterative approach to avoid the overhead of recursive function calls and prevent stack overflows. Additionally, optimizing the space complexity by reusing a smaller number of variables instead of maintaining large DP tables can also improve performance.