AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work introduces π -Drive, a driving version of π 0 .
Fire-VLA is introduced, a failure-informed self-evolution framework that converts unresolved failures into privileged supervision for the next policy in autonomous-driving vision-language-action models.
A unified view of post-training for autonomous driving is presented by defining its scope and organizing the existing literature into four major families based on the form of supervision they use, which aim to facilitate a systematic understanding of this emerging area and stimulate future research on reliable and effi...
SimWAM, a simple yet effective WAM that leverages future-video prediction as a training-time supervision signal, co-trains a pretrained video expert and a lightweight action expert with joint flow matching and applies reinforcement learning to optimize a compositional driving reward beyond trajectory imitation.
Two systematic attempts to improve large pretrained models with minimal or zero modification to their weights via reinforcement learning on a frozen OpenVLA-7B using binary task-success rewards on LIBERO-Goal reveal a common ceiling.
This survey reviews this transition across behavior cloning, conditional imitation learning, privileged distillation, BEV and vectorized planning, unified perception-prediction-planning architectures, world-model-based planners, and vision-language-action systems, finding open challenges in uncertainty-aware planning,...
Across three flow-based VLA models on multiple simulated manipulation benchmarks and two real-world tasks, StructRL improves exploration efficiency and OOD performance over prior in-chain baselines, demonstrating the effectiveness of structured action-space exploration for adapting flow-based VLA with RL.
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