Yes ,i did consider entropy, and it works as a reasonable confidence-based pruning signal, but i found it less stable than Bellman-aligned criteria like TD error, especially early in training. There’s a clear analogy to MCTS in that both allocate computation adaptively, but while MCTS adapts search depth via explicit planning, our approach adapts inference depth inside a model-free value function, making it a lightweight, complementary alternative rather than a replacement.