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What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study

Paper recorded by Signals 4 on 2026-10-13 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-13 on arXiv · recorded by Signals 4 on 2026-10-14

Category: cs.AI · 人工智能 · first seen 2026-10-14

Abstract

How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filtering, and conditioning across reward definitions and label granularities using 5,000 naturally suboptimal human demonstrations and 24 target tasks. Within each VLA, we match demonstrations and training budgets. Additional comparis

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#322 most recent of 960 cs.AI papers we have recorded · ↑ newer: ChunkAct: Action Chunking for Agentic Robotics · ↓ older: Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rew
Cite this page: What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study: the #322 most recent of 960 cs.AI papers we have recorded (as of 2026-10-13). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/what-matters-in-quality-aware-vla-pretraining-a-benchmark-and-empirical-study.html
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