The Challenge of Myopic Agent Behavior

Standard reinforcement learning for LLM agents often struggles with multi-turn tasks because models tend to optimize for immediate rewards rather than long-term goal completion. This "myopia" leads to agents that may perform well on individual steps but fail to maintain a coherent strategy across complex, multi-step workflows. PlanPO (Group Planning-Aware Policy Optimization) addresses this by shifting the focus from individual action optimization to group-level planning awareness.

Integrating Planning into Policy Optimization

PlanPO introduces a framework where the agent's policy is explicitly conditioned on a planning objective. Instead of treating each turn as an isolated decision, the model evaluates potential trajectories by considering the global plan. By grouping similar task trajectories, the algorithm allows the model to learn from the collective success or failure of a plan, rather than just the isolated outcome of a single action. This approach effectively bridges the gap between high-level strategic planning and low-level execution, forcing the model to reconcile its immediate outputs with the broader requirements of the task.

Performance and Strategic Alignment

By incorporating planning awareness, PlanPO reduces the frequency of "hallucinated" or redundant steps that often plague multi-turn agents. The research demonstrates that agents trained with this method show higher success rates in complex environments where state changes are cumulative. The core insight is that by optimizing the policy to be "planning-aware," the model develops a more robust internal representation of task progression, leading to more reliable performance in long-horizon reasoning tasks.