Output-Level Regularization Eliminates the Seed Lottery in Single-GPU VLA Fine-Tuning
A new study reveals the 'seed lottery' in single-GPU fine-tuning of vision-language-action models: out of 13 identical runs with different seeds, one silently degrades to 65.2% success rate from 91-94%. The culprit is output collapse, where the action predictor produces nearly identical outputs regardless of input. Weight-level regularizers fail to detect this, but output-level regularizers (VICReg, Dropout, or halved learning rate) eliminate all catastrophic seeds. The simplest fix is changing one number in the optimizer config.
[2606.13856] Output-Level Regularization Eliminates the Seed Lottery in Single-GPU VLA Fine-Tuning
[Submitted on 11 Jun 2026]
Title:Output-Level Regularization Eliminates the Seed Lottery in Single-GPU VLA Fine-Tuning
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Abstract:Fine-tuning a vision-language-action model (VLA-JEPA) on a single GPU should be simple: load a pretrained checkpoint, run training, deploy. There is a hidden danger. Run the same fine-tuning code thirteen times -- same data, same architecture, different random seed -- and twelve runs produce a robot succeeding 91--94% of the time, while one run silently degrades to 65.2%: a 29 pp gap with no error message, no warning, and no way to predict which seed will fail. We call this the seed lottery. We trace the cause to output collapse: the action predictor quietly learns to produce nearly identical outputs regardless of what the robot sees. Existing weight-level methods (L2, EWC) are structurally blind to this collapse -- they penalize weight changes, but collapse occurs in directions weights can move freely without affecting outputs, a gap we formalize via the Jacobian null-space. Across 7 methods x up to 13 seeds x 3 LIBERO benchmarks, three output-level regularizers -- VICReg (n=12 seeds), Dropout (n=4), and a halved learning rate (n=5) -- each eliminate every catastrophic seed (0/21 combined collapses vs. 1/13 Baseline; F(12,11)=28.7, p
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