Can the Environment Speak for Itself? $T^{2}$-GRPO: A Turn-Trajectory Group Relative Policy Optimization for Caregiver Agents
arXiv:2606.08875v1 Announce Type: new Abstract: Optimizing large language models (LLMs) for long-horizon caregiver agents requires balancing delayed task objectives with immediate environment dynamics, such as patient distress and resistance. In dementia care, this balance is especially difficult: trajectory level rewards are too sparse for turn level credit assignment, while external LLM-based evaluators are costly and can misread fragmented or indirect patient responses. To address this issue, we propose \textbf{T}urn-\textbf{T}rajectory \textbf{G}roup \textbf{R}elative \textbf{P}olicy \text
The increasing sophistication of LLMs and the pressing need for effective, automated care solutions in an aging global population are driving innovation in caregiver agents.
This development improves autonomous agent capabilities for complex, long-horizon tasks requiring nuanced interaction, directly impacting the deployment and reliability of AI in sensitive real-world applications.
The ability to extract turn-level rewards from sparse trajectory data and handle indirect patient responses fundamentally enhances the training and efficacy of AI caregiver agents.
- · AI healthcare providers
- · Elderly care technology developers
- · LLM developers
- · AI agent researchers
- · Traditional AI evaluation methods for complex tasks
- · Labor-intensive human caregiver training relying solely on direct feedback
More robust and adaptable AI agents become viable for increasingly complex and sensitive human-centric tasks.
Accelerated development and adoption of AI in sectors requiring high-stakes, nuanced interactions, such as healthcare and education.
Ethical and regulatory frameworks for autonomous AI agents in care settings will need rapid evolution to keep pace with technological capabilities.
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Read at arXiv cs.AI