SmolVLA
Open weightsHugging Face
2 of 6 axes published · values checked against their primary sources on 23 Oct 2026
Success
Does it do the trained task? (% of trials, trained setup)
32.5%
Independent benchmark. SmolVLA's own paper reports 78.3% on its own SO-100 tasks (vs 61.7% for π0).
Source: SO-101 VLA benchmark, arXiv 2606.08881 · Results table, average
Robustness
Does it survive the cell drifting? (% under the perturbation set)
Owed
Not reported. SmolVLA is not in LIBERO-Plus or IndustrialVLA-Bench.
Instruction
Does rewording the instruction break it? (% under paraphrase)
Recovery
Does it fix its own mistakes? (% of injected failures recovered)
3.2%
Share of failures recovered without help.
Source: SO-101 VLA benchmark, arXiv 2606.08881 · Recovery results
Speed
Is it as fast as a trained person? (human cycle time ÷ robot cycle time)
Owed
No human comparison. Its own paper: asynchronous inference finishes tasks 30% faster than synchronous (9.7 s vs 13.75 s) — against itself, not a person.
Checked: SmolVLA paper, arXiv 2506.01844
Interventions
How often does a person step in? (per 1,000 cycles)
Built on SmolVLA
Published work that starts from SmolVLA: fine-tunes, add-ons and models trained from it. Their results are listed here, apart from SmolVLA's own values above, with the source each one comes from.
Backtrack
Add-onStanford · ETH Zurich · 23 Oct 2026
A small add-on spots a failing attempt, reverses the arm's last moves and lets the unchanged model try again.
- Success86.7%+3.4 pts vs SmolVLA in the same paper (83.3%)Simulation · LIBERO, 4 suites · Grade BRun with non-default inference settings and twice LeRobot's step budget (§4.2).
- Recovery20.3%Simulation · LIBERO, 4 suites · Grade BShare of the base model's failures turned into successes.
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