Relative vs absolute actions, MeanStd normalization, L2 vs L1: the ablations in the new LingBot-VLA 2.0 paper are a tidy VLA training cheat sheet

I spent the weekend reading through the LingBot-VLA 2.0 paper from Robbyant and came away treating the ablation section like a personal cookbook. The clean comparisons they ran on four GM-100 real-robot tasks are immediately useful for VLA training.

Relative joint actions outperformed absolute by a wide margin, jumping average success from 33.7% to 55.0%. The mechanism is straightforward: relative targets compress the action scale to about 0.3x and center the distribution around zero. That makes the regression problem far cleaner. MeanStd normalization beat MinMax and Q01-Q99 by a similar margin, 55.0% versus 47.5% and 47.4%. It preserves the widest effective dynamic range, so the long-tailed corrective motions survive instead of being squeezed into the narrow band MinMax and Q01-Q99 compress everything into. L2 loss edged out L1 at 55.0% versus 46.4%. That makes sense once you notice most relative targets are small corrections near zero where squared error focuses the gradient. The one split result was action space: joint won barcode scanning (58.7 vs 24.0) while EEF won contact-rich ketchup squeezing (81.7 vs 41.7), so there is no universal answer there.

These numbers need context. Even the best configuration averages only about 55% success on these four tasks, and the underlying model sits at 15-34% on the harder generalist benchmarks per the authors’ own GM-100 eval. Several tasks still score 0%. The paper itself notes the model often makes partial progress but fails at the final precise placement or release step. Out-of-distribution performance degrades sharply. These ablation gains are real but relative, not a solved problem. Section 6 has the full tables.

One thing I am still chewing on: is the relative action win really about scale compression, or is local-motion regression just an inherently easier target regardless of scale?