This paper evaluates whether factor-wise auxiliary dynamics supervision produces useful latent structure or improved robustness in simulated humanoid locomotion. We introduce DynaMITE, a transformer encoder with a factored 24-dimensional latent space trained using per-factor auxiliary losses during proximal policy optimization. Our method is compared against LSTM, plain Transformer, and MLP baselines on a Unitree G1 humanoid robot across four Isaac Lab tasks. Through comprehensive ablation studies with 10 random seeds, we analyze the contributions of tanh bottlenecks and auxiliary losses to in-distribution reward performance. Results demonstrate that the supervised latent fails to produce decodable or functionally separable factor structure, with probe R-squared near zero and minimal reward changes when subspaces are clamped.