Steering Agent
A supervised agent framework for monitoring the execution of LLM agents. Through a dual fast-and-slow path mechanism it detects goal drift in real time and generates intervention actions, and it is trained jointly with reinforcement learning and ranking objectives. It improves the performance and efficiency of single- and multi-agent systems without altering the original system architecture.
Timeline (last 90 days)
The paper proposes a supervisory agent called Steering Agent, which observes the execution context in real time, judges whether goal drift has occurred, and decides whether to intervene.
Steering Agent adopts a dual-path intervention mechanism: a fast path decides when to intervene at low cost, and a slow path decides how to intervene at high cost.
Steering Agent is trained with joint reinforcement learning optimization, and its objective function combines a pairwise ranking objective and Group Relative Policy Optimization (GRPO).
The training objective of Steering Agent is not to replace the original executor but to supervise it from the side, keeping the original system architecture unchanged.
The paper validated Steering Agent on information retrieval search tasks, and the experimental results show that it can provide effective supervision for single-agent and multi-agent systems and improve performance and efficiency.
The supervisory capability learned by Steering Agent can generalize across tasks and transfer to out-of-domain tasks.
Steering Agent can adjust the intervention frequency without retraining, providing flexibility for practical deployment.
Steering Agent judges drift by observing the execution context rather than the internal parameters of the executor, and therefore has a certain degree of model agnosticism.
All Facts (8)
The paper proposes a supervisory agent called Steering Agent, which observes the execution context in real time, judges whether goal drift has occurred, and decides whether to intervene.
50%UnverifiedSteering Agent adopts a dual-path intervention mechanism: a fast path decides when to intervene at low cost, and a slow path decides how to intervene at high cost.
50%UnverifiedSteering Agent is trained with joint reinforcement learning optimization, and its objective function combines a pairwise ranking objective and Group Relative Policy Optimization (GRPO).
50%UnverifiedThe training objective of Steering Agent is not to replace the original executor but to supervise it from the side, keeping the original system architecture unchanged.
50%UnverifiedThe paper validated Steering Agent on information retrieval search tasks, and the experimental results show that it can provide effective supervision for single-agent and multi-agent systems and improve performance and efficiency.
50%UnverifiedThe supervisory capability learned by Steering Agent can generalize across tasks and transfer to out-of-domain tasks.
50%UnverifiedSteering Agent can adjust the intervention frequency without retraining, providing flexibility for practical deployment.
50%UnverifiedSteering Agent judges drift by observing the execution context rather than the internal parameters of the executor, and therefore has a certain degree of model agnosticism.
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