arXiv:2606.31392v1 Announce Type: new Abstract: Tool-augmented vision-language models (VLMs) can solve multimodal, multi-step tasks by calling external tools, yet they remain fragile in practice. Existing works have two common gaps. Supervised fine-tuning (SFT) is built mostly on successful trajectories and offers little signal for recovery after tool failures, while sparse trajectory-level RL rewards provide limited guidance on which step failed and how to repair it. We introduce ReGRPO (Reflection-augmented Group Relative Policy Optimization), a framework that learns reflection-guided correc
The rapid advancement of large language models and vision-language models necessitates more robust and reliable agentic behaviors for real-world application, making tool-using agents a critical next frontier.
Improving the reliability and resilience of AI agents in complex, multi-step tasks with external tools is crucial for their deployment in critical applications and collapsing white-collar workflows.
This framework significantly reduces the fragility of tool-augmented agents, enabling them to recover from failures and learn more effectively without extensive human supervision in dynamic environments.
- · AI Agent developers
- · SaaS companies integrating AI agents
- · Industries with complex operational workflows
- · Enterprises seeking automation at scale
- · Manual workflow providers
- · Companies reliant on fragile, non-adaptive automation
- · Supervised fine-tuning (SFT) as a sole method for agent training
More reliable and capable AI agents will be deployed across a wider range of industries, automating increasingly complex tasks.
This deployment will lead to significant productivity gains and potentially reshape job functions within white-collar sectors.
The enhanced autonomy and resilience of AI agents could accelerate the development of synthetic general intelligence and further blur human-AI interaction boundaries.
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Read at arXiv cs.AI