TRAC: Trajectory-Reliable Alignment Training for Action Chunks in VLA Models

Action chunking predicts a sequence of future actions for execution between policy queries. Small action errors can accumulate and alter the intended motion, making relations across the sequence important for reliable manipulation. However, standard point-wise prediction losses sum penalties at individual action positions without explicitly evaluating cumulative deviations or coordinated variation. We introduce TRAC, Trajectory-Reliable Alignment Training for Action Chunks in VLA Models, which treats action chunks as structured action-space trajectories. TRAC complements flow matching with two objectives: weighted prefix consistency aligns accumulated translations with demonstrations throughout the chunk, while a local residual-structure prior encourages coordinated variation among neighboring actions. Both objectives operate on clean-action estimates, preserving the backbone's architecture and inference procedure. On LIBERO, TRAC achieves 96.55% success with SmolVLA, improving the original policy by $10.05$ percentage points and a clean-action baseline by 3.40 points. Further evaluations show lower cumulative prediction errors and gains across chunk lengths, with additional improvements on LIBERO-Plus, in an adapted GR00T policy, and on four real-robot tasks.