TIDAL: Temporally Interleaved Diffusion and Action Loop for High-Frequency VLA Control

• URL: http://arxiv.org/abs/2601.14945v1
• Date: Wed, 21 Jan 2026 12:43:11 GMT
• Title: TIDAL: Temporally Interleaved Diffusion and Action Loop for High-Frequency VLA Control
• Abstract summary: Large-scale Vision-Language-Action (VLA) models offer semantic generalization but suffer from high inference latency.<n>We propose TIDAL, a hierarchical framework that decouples semantic reasoning from high-frequency actuation.<n> TIDAL operates as a backbone-agnostic module for diffusion-basedVLAs, using a dual-frequency architecture.
• Score: 15.534182843429043
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large-scale Vision-Language-Action (VLA) models offer semantic generalization but suffer from high inference latency, limiting them to low-frequency batch-and-execute paradigm. This frequency mismatch creates an execution blind spot, causing failures in dynamic environments where targets move during the open-loop execution window. We propose TIDAL (Temporally Interleaved Diffusion and Action Loop), a hierarchical framework that decouples semantic reasoning from high-frequency actuation. TIDAL operates as a backbone-agnostic module for diffusion-based VLAs, using a dual-frequency architecture to redistribute the computational budget. Specifically, a low-frequency macro-intent loop caches semantic embeddings, while a high-frequency micro-control loop interleaves single-step flow integration with execution. This design enables approximately 9 Hz control updates on edge hardware (vs. approximately 2.4 Hz baselines) without increasing marginal overhead. To handle the resulting latency shift, we introduce a temporally misaligned training strategy where the policy learns predictive compensation using stale semantic intent alongside real-time proprioception. Additionally, we address the insensitivity of static vision encoders to velocity by incorporating a differential motion predictor. TIDAL is architectural, making it orthogonal to system-level optimizations. Experiments show a 2x performance gain over open-loop baselines in dynamic interception tasks. Despite a marginal regression in static success rates, our approach yields a 4x increase in feedback frequency and extends the effective horizon of semantic embeddings beyond the native action chunk size. Under non-paused inference protocols, TIDAL remains robust where standard baselines fail due to latency.

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