Gym-V: A Unified Vision Environment System for Agentic Vision Research

ArXi:2603.15432v1 Announce Type: new As agentic systems increasingly rely on reinforcement learning from verifiable rewards, standardized ``gym'' infrastructure has become essential for rapid iteration, reproducibility, and fair comparison. Vision agents lack such infrastructure, limiting systematic study of what drives their learning and where current models fall short. We