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

• URL: http://arxiv.org/abs/2512.04949v1
• Date: Thu, 04 Dec 2025 16:15:46 GMT
• Title: CARL: Critical Action Focused Reinforcement Learning for Multi-Step Agent
• Authors: Leyang Shen, Yang Zhang, Chun Kai Ling, Xiaoyan Zhao, Tat-Seng Chua,
• Abstract summary: We propose CARL, a critical-action-focused reinforcement learning algorithm tailored for multi-step agents.<n>Carl achieves both stronger performance and higher efficiency during training and inference across diverse evaluation settings.
• Score: 53.56274149236814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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