arXiv:2605.20246v1 Announce Type: new Abstract: Recently, vision-language model (VLM) agents have shown promising progress in open-world tasks, where successful task completion often requires multiple turns of visual perception and action execution. However, existing methods still rely primarily on Supervised Fine-Tuning (SFT) with expert demonstrations, while the advanced reinforcement learning (RL) algorithm, specifically Group Relative Policy Optimization (GRPO), has not been effectively employed for multi-turn RL in these tasks because standard GRPO requires full trajectories as training s
This paper addresses a key limitation in current VLM agent development by integrating advanced reinforcement learning directly into open-world task completion.
Improving VLM agents with sophisticated RL techniques like GRPO could accelerate their capability to perform complex, multi-step tasks in dynamic environments, moving closer to truly autonomous systems.
The ability of VLM agents to learn efficiently from complex interactions, rather than relying solely on supervised expert demonstrations, is enhanced, broadening their potential application space.
- · AI research labs
- · Robotics companies
- · Software developers
- · Automation industries
- · Tasks requiring manual multi-turn human intervention
More capable and robust open-world VLM agents will emerge.
This could lead to substantial advancements in autonomous robotics and AI assistants.
The acceleration of AI agents may disrupt various white-collar workflows and services.
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Read at arXiv cs.LG