Filter Before Mixing: Per-Modality Denoising for Multimodal RL with Application to Health Management ^†

Multimodal reinforcement learning agents must fuse signals with vastly different noise profiles—yet existing architectures, whether monolithic ($\pi$0, DreamerV3) or modular (MSDP, VTDexManip), allow noise from unreliable modalities to contaminate reliable ones at the point of fusion. We propose filter before mixing: each modality’s representation is independently refined by a per-modality Flow Matching module before spectral-domain fusion via a Fourier Neural Operator (FNO) with a residual gate ensuring that refinement is never harmful. The resulting architecture, FreamerV1 (Filter-before-mixing dreamer), has 93M parameters (0.4M trainable). On MiniGrid, FreamerV1 reaches 87.7 ± 8.2% (3 seeds) at 5000 episodes, while the encoder-only baseline degrades to 78% due to catastrophic forgetting. With OGM-GE (On-the-fly Gradient Modulation) for adaptive per-modality gate control, FreamerV1 achieves an 8.0% relative improvement in success rate over manual tuning with halved seed-to-seed variance (three seeds). On Crafter (no language modality), it achieves an 11.7% relative improvement over DreamerV3 in the official Crafter score (geometric mean of 22 achievement success rates; 10 seeds). On PAMAP2 wearable sensors—where no pretrained encoder exists—the foundation encoder achieves 2.4× higher reward and 16× lower variance than a vanilla MLP, confirming that the filter-before-mixing advantage grows with encoder noise.