Vision‑language mixture‑of‑experts still choke on high‑resolution images because the router treats every token identically, letting image patches overwhelm their expert lanes while text tokens sit idle. Geometry‑guided routing flips that script by carving out separate pathways for visual and linguistic tokens, restoring balance while maintaining comparable downstream quality.
The prevailing solution, the standard token‑level Switch auxiliary loss (Std‑Aux), only equalizes the total mixed load; as a result, large image loads can cancel against text loads, producing more than a fivefold swing in imbalance when resolution changes [1]. This hidden cancellation leaves practitioners blind to modality‑specific overloads until performance degrades.
“Across four split backbones, ReBA lowers benchmark‑input load with comparable mean task quality.” The new scheduler enforces a modality boundary and an equal‑weight per‑image instance, so each image’s patches share the same routing decision while text tokens are handled independently [1].
“ReBA lowers mean layer CV for every benchmark and backbone.” By smoothing the coefficient of variation across layers, ReBA guarantees that no single expert becomes a bottleneck, regardless of how many high‑resolution patches appear in a batch [1].
