TL;DR
This paper applies RECAP’s three-component recipe to Ψ 0 Wei et al. (2026), a recently open-sourced humanoid VLA, providing the first reproduction of RECAP outside Physical Intelligence’s stack.
It is suggested that LoRA at r=32 with full vision encoder fine-tuning is a practical approach, reducing static peak VRAM from 36.2 to 10.8 GiB (parameters and optimizer states, activation memory excluded) without detectable performance loss.
A cache-efficient lifelong Vision-Language-Action learning framework for robotic manipulation, which alleviates the plasticity-stability trade-off with a dual-timescale adaptation mechanism while achieving low-cost robotic deployment with a cache-efficient replay strategy.
Xiao-Robotics-1 serves as a strong robot foundation policy that can be efficiently fine-tuned on complex, dexterous tasks with high data efficiency and across multiple simulation benchmarks, Xiaomi-Robotics-1 outperforms state-of-the-art methods.
SIEVE, a structure-aware data selection method for VLA imitation learning that can surpass full-data training while using only 50% of demonstrations and 50% of training steps, suggests that reusable structure, captured through primitives and transitions, is an important signal for efficient VLA imitation learning.
This paper proposes a self-supervised method that generates online interaction rollouts from the zero-shot VLA as additional training data for finetuning and demonstrates the success of this approach across test sets probing generalization on a real ALOHA robot and a new simulation benchmark in RoboTwin.
A mechanism-by-mechanism transition to the recurrent behavior-cloning baseline shows that replacing the predictive pathway with direct observation input produces the largest single performance drop, accounting for approximately $70\% of the endpoint gap.
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