arXiv:2603.00656v2 Announce Type: replace Abstract: Real-world user requests to LLM agents are often underspecified. Agents must interact to acquire missing information and make correct downstream decisions. However, current multi-turn GRPO-based methods often rely on trajectory-level reward computation, which leads to credit assignment problems and insufficient advantage signals within rollout groups. A feasible approach is to identify valuable interaction turns at a fine granularity to drive more targeted learning. To address this, we introduce InfoPO (Information-Driven Policy Optimization)
The increasing complexity of real-world AI applications and the drive towards more autonomous agents necessitate advanced optimization methods to handle underspecified requests efficiently.
This research addresses a core challenge in current AI agent development, promising more robust and user-centric LLM agents capable of sophisticated interaction and decision-making.
The introduction of InfoPO could lead to more effective multi-turn interaction systems, mitigating credit assignment problems and improving the learning efficiency of AI agents.
- · AI Agent developers
- · Companies deploying LLM agents for customer service
- · Researchers in reinforcement learning
- · Legacy multi-turn interaction systems
- · Methods reliant on trajectory-level reward computation
Improved performance and reliability of AI agents in handling complex, ambiguous user requests.
Accelerated adoption of AI agents across various industries due to enhanced user experience and functionality.
Deeper integration of AI agents into critical workflows, potentially restructuring how information is accessed and tasks are completed.
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Read at arXiv cs.AI