Efficient data acquisition is a key factor in neural 3D reconstruction, where redundant viewpoints can increase computational cost without improving reconstruction quality. We propose a reinforcement learning-based active viewpoint policy that optimises image collection for NeRF reconstruction under a fixed sensing budget. The agent is trained to maximise scene coverage while penalising redundant observations and inefficient trajectories, and learns to terminate data collection as information gain saturates. Evaluated on indoor scenes from the Replica dataset, our method achieves 33.33 dB PSNR while reducing the number of views by 25% (from 80 to 60), with a proportional reduction in scene acquisition time. Under the same sensing budget, it outperforms a coverage-only policy (33.05 dB) and a heuristic baseline (18.79 dB). The learned policy generalises to unseen scenes without retraining. These results indicate that learned data acquisition can improve both efficiency and reconstruction quality as an upstream component of 3D pipelines.