CARL: Critical Action Focused Reinforcement Learning for Multi-Step Agent

2025-12-04

AI Summary

In multi-step agent tasks, conventional policy optimization methods assume uniform contribution of all actions to the final return, overlooking the dominant role of critical actions—leading to inefficient training and slow convergence. To address this, we propose Critical Action Reinforcement Learning (CARL), a reinforcement learning framework that focuses policy updates exclusively on high-impact actions. CARL introduces a learnable action importance scoring module that dynamically identifies critical actions; only these actions receive fine-grained gradient updates, substantially reducing noise interference and computational redundancy. Low-importance actions are deliberately excluded from policy updates, enabling more precise and resource-efficient optimization. Experiments across diverse complex multi-step benchmarks demonstrate that CARL significantly improves convergence speed, final task performance, and cross-task generalization—effectively resolving the core challenge of imbalanced gradient signal allocation in sequential decision-making.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Agents capable of accomplishing complex tasks through multiple interactions with the environment have emerged as a popular research direction. However, in such multi-step settings, the conventional group-level policy optimization algorithm becomes suboptimal because of its underlying assumption that each action holds equal contribution, which deviates significantly from reality. Our analysis reveals that only a small fraction of actions are critical in determining the final outcome. Building on this insight, we propose CARL, a critical-action-focused reinforcement learning algorithm tailored for multi-step agents. CARL achieves focused training through providing action-level optimization signals for high-criticality actions while excluding low-criticality actions from model update. Extensive experiments demonstrate that CARL achieves both stronger performance and higher efficiency during training and inference across diverse evaluation settings.

Problem

• Identifies critical actions in multi-step agent tasks
• Optimizes training by focusing on high-criticality actions
• Enhances performance and efficiency in reinforcement learning

Innovation

• Focuses training on critical actions only
• Excludes low-criticality actions from updates
• Provides action-level optimization signals