cs.ROOct 23, 2026
SaveTTT in VLA Models: Exploring Training with Human Demonstrations
Organizations: Standard Robots Co., Ltd.
Abstract
Long-horizon robot tasks require retaining and using past information. We integrate Test-Time Training (TTT), which stores context in fast weights, into InternVLA-A1 and Pi0.5 to study action generation conditioned on human demonstrations through training on paired demonstrations and robot trajectories. We implement memory transfer between the two phases and truncated backpropagation, and examine training objectives, gradient coverage, and contrastive regularization across video, short-video, and single-frame cue tasks. In our experiments, adding TTT and training on paired data at the current scale did not lead to consistent target selection based on demonstrations. Under the tested settings, shorter sequences, full trajectory gradient connectivity, and contrastive regularization did not clearly improve responsiveness to demonstrations. When demonstrations were reduced to single-frame cues, real-robot execution still showed a preference for cups at fixed positions. We document the implementation, training configurations, and observations to support further inspection and replication.
Figures & tables
Figure 1: TTT layer architecture and memory operations. Green modules contain slow weights, while red modules represent a two-layer GELU MLP parameterized by fast weights. Normalization and projections transform the input into , , and . The right path predicts from and updates fast weights using the inner loss. The left path queries the updated model with , then applies an output projection and a gated residual connection. The two MLPs depict two calls to the same inner model, before and after the update. The batch dimension, head splitting, and inner normalization and residual details are omitted for clarity.
Figure 2: TBPTT and gradient accumulation in our implementation. Yellow boxes at the top are sampled decision anchors, each predicting an action chunk and computing its loss. Vertical dashed lines separate segments; yellow arrows indicate gradient contributions within segments and accumulation across segments. After the full trajectory batch is processed, accumulated gradients update slow weights, as indicated by the green arrow at the bottom. Inner fast-weight updates and state transfer across segments are not drawn separately.
Figure 3: Memory operations in human demonstrations and robot trajectories. Blue denotes the human phase, and green denotes the robot phase. Green arrows indicate fast-weight updates; yellow arrows indicate memory use in action-chunk prediction. The human phase may write more frequently, while the robot phase advances at action-chunk intervals. Fast weights carry across the phase boundary. Arrow density only illustrates that the frequencies can differ; it does not specify a fixed ratio.
Figure 4: Misalignment under two state-input conditions. In the upper GT-state case, earlier memory carries forward, but subsequent states still come from demonstrations rather than the execution of the model’s decisions. In the lower AR-state case, predicted actions feed into subsequent states, while visual observations still come from demonstrations and may conflict with the propagated states. The predicted trajectories and memory illustrate these relationships, not actual environment interactions during training.
| Model | GT-state MAE | AR-state MAE | Real-robot completion rate |
|---|---|---|---|
| baseline delta | 0.023604 | 0.099308 | 90% |
| TTT delta | 0.022631 | 0.091122 | 73% |
| TTT abs | 0.028153 | 0.082412 | 64% |
Table 1: Warm-up comparison after 6,000 training steps.
| Data setting | Dataset size |
|---|---|
| InternVLA-A1 backbone pretraining | Approximately 692 million frames |
| Original video demonstration task | 654 pairs, 556,302 frames; 621 training pairs |
| Short-video demonstration task | 273 pairs; 264 training pairs |
| Single-frame cue task | 210 pairs; 200 training pairs |
Table 2: Backbone pretraining and demonstration-conditioned task data.
Appendix figures & tables7 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Incremental and absolute action targets during state autoregression. The gray straight line denotes the demonstration trajectory, and the dark gray curve denotes deviation caused by the previous action chunk. On the left, green arrows apply the original increments to the deviated state, potentially preserving the existing position error. On the right, red arrows keep absolute targets at the demonstrated positions. The figure illustrates target geometry, not learned corrective behavior during real execution.
| Action representation | Training target relative to the ground-truth state | Absolute action if the same value is output after deviation |
| delta | 0.0 | |
| abs | 1.0 | 1.0 |
Table 3: Action representations after state deviation.
Figure 6: Computation of the contrastive regularizers. First, each human demonstration is read, its terminal fast weights and graph are retained, and the memory separation regularizer is computed. Second, correct robot pairings are processed to compute the base training loss. Cross-pairings are then evaluated for the trajectory contrastive regularizer. Horizontal arrows indicate computation order; each robot branch restarts from the corresponding demonstration’s terminal memory rather than inheriting updates from the preceding robot branch.
| Setting | Value |
|---|---|
| Learning rate | |
| Learning-rate warm-up steps | 50 |
| Weight decay | 0.01 |
| Parallel trajectories per GPU | 2 |
| Generation loss weight | 0.01 |
| Fast-weight computation precision | FP32 |
Table 4: Settings for the contrastive regularization comparison.
| Training setup | Training GT-state MAE | Training AR-state MAE | Validation GT-state MAE | Validation AR-state MAE |
|---|---|---|---|---|
| Standard training (10,000 steps) | 0.007650 | 0.024754 | 0.041284 | 0.107719 |
| Contrastive regularization (10,000 steps) | 0.008720 | 0.039244 | 0.044833 | 0.118272 |
Table 5: Open-loop MAE after 10,000 training steps.
| Training setup | GT-state | AR-state | ||||
|---|---|---|---|---|---|---|
| With demonstration | Without demonstration | Demonstration benefit | With demonstration | Without demonstration | Demonstration benefit | |
| Standard training (10,000 steps) | 0.041284 | 0.041287 | +0.0064% | 0.107719 | 0.107573 | 0.1362% |
| Contrastive regularization (10,000 steps) | 0.044833 | 0.044785 | 0.1072% | 0.118272 | 0.118382 | +0.0930% |
Table 6: Validation MAE with and without demonstrations after 10,000 training steps.
| Training setup | Cup-selection success rate |
|---|---|
| Standard training (10,000 steps) | 25% |
| Contrastive regularization (10,000 steps) | 12.5% |
Table 7: Real-robot cup-selection success after 10,000 training steps.