Rotate back to portrait for the best experience.

02 — Data · AI Model Performance

SmolVLA

Open weightsHugging Face

2 of 6 axes published · values checked against their primary sources on 23 Oct 2026

  1. Success

    Does it do the trained task? (% of trials, trained setup)

    32.5%

    Real robotGrade B · Paper or report with method (rubric or error bars)80 trialsSO-101 · 4 tasks

    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

  2. Robustness

    Does it survive the cell drifting? (% under the perturbation set)

    Owed

    Not reported. SmolVLA is not in LIBERO-Plus or IndustrialVLA-Bench.

    Checked: IndustrialVLA-Bench, arXiv 2609.25562

  3. Instruction

    Does rewording the instruction break it? (% under paraphrase)

    Owed

    Not reported.

    Checked: SmolVLA paper, arXiv 2506.01844

  4. Recovery

    Does it fix its own mistakes? (% of injected failures recovered)

    3.2%

    Real robotGrade B · Paper or report with method (rubric or error bars)SO-101

    Share of failures recovered without help.

    Source: SO-101 VLA benchmark, arXiv 2606.08881 · Recovery results

  5. 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

  6. Interventions

    How often does a person step in? (per 1,000 cycles)

    Owed

    Not reported.

    Checked: SmolVLA paper, arXiv 2506.01844

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.

  1. Backtrack

    Add-on

    Stanford · 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.

    Source: Backtrack paper, arXiv 2610.32941

Know of one we missed? Send it with its source.

Get notified

New results on AI Model Performance, and news from B2A. Nothing else.