Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
Addresses the inference bottleneck of dual-system VLA models for robotic manipulation by reducing computational cost without sacrificing performance.
Latent Bridge predicts VLM output deltas between timesteps to reduce VLM calls by 50-75% while retaining 95-100% performance, achieving 1.65-1.73x speedup across multiple robotic manipulation benchmarks.
Dual-system Vision-Language-Action (VLA) models achieve state-of-the-art robotic manipulation but are bottlenecked by the VLM backbone, which must execute at every control step while producing temporally redundant features. We propose Latent Bridge, a lightweight model that predicts VLM output deltas between timesteps, enabling the action head to operate on predicted outputs while the expensive VLM backbone is called only periodically. We instantiate Latent Bridge on two architecturally distinct VLAs: GR00T-N1.6 (feature-space bridge) and π0.5 (KV-cache bridge), demonstrating that the approach generalizes across VLA designs. Our task-agnostic DAgger training pipeline transfers across benchmarks without modification. Across four LIBERO suites, 24 RoboCasa kitchen tasks, and the ALOHA sim transfer-cube task, Latent Bridge achieves 95-100% performance retention while reducing VLM calls by 50-75%, yielding 1.65-1.73x net per-episode speedup.