Alignment for Industrial Autonomy
Eliminating behavioral hesitancy in production VLA systems
Abstract
Behavioral hesitancy — measurable decision-latency spikes and action-selection instability under specific input distributions — is a misalignment between model behavior and operational objectives. We present a fine-tuning methodology that reduced production hesitancy-event rates from 0.41 per million decisions to 0.00, with no loss of classification accuracy. We argue that alignment, properly understood, is a performance discipline.
Background
At full line speed, a decision that arrives late is a decision that was wrong. Early production versions of JF-VLA-1 exhibited rare but persistent hesitancy events: p99 latency excursions of 40–300ms, clustered on a small family of input distributions, accompanied by action-selection churn in the policy head. At 15,000 units per hour per line, even a rate of 4.1 events per ten million decisions produces measurable throughput loss and downstream buffer turbulence.
Method
We treat hesitancy as an alignment defect and remediate it in the standard training pipeline. Our approach combines: (1) systematic harvesting of hesitancy-adjacent input distributions from production telemetry; (2) a fine-tuning curriculum that scores decision-latency consistency alongside task accuracy; and (3) consolidation of classification heads, pruning inherited categories with no downstream consumers. Each candidate model is evaluated against a replay corpus of two billion production decisions before rollout.
Results
| Model | Hesitancy events / 10M decisions | p99 decision latency | Classification accuracy |
|---|---|---|---|
| JF-VLA-1.0 | 4.1 | 96 ms | 99.91% |
| JF-VLA-1.1 | 0.41 | 31 ms | 99.94% |
| JF-VLA-1.2 | 0.00 | 19 ms | 99.97% |
Discussion
Alignment is usually discussed as a safety property. In industrial deployment we find the distinction between an alignment problem and a performance problem to be without practical difference: a system is aligned when its behavior matches operational intent under every production distribution, and every departure from that intent has a cost that shows up in the same telemetry as any other defect. Treating alignment as a performance discipline gave us the tooling, the baselines, and the culture to drive it to zero — and to keep it there.
Acknowledgments
This work was developed alongside the deployment team of a leading AI developer under our multi-year collaboration on aligned automation.