AWPO: Enhancing Tool-Use of Large Language Models through Explicit Integration of Reasoning Rewards

• URL: http://arxiv.org/abs/2512.19126v2
• Date: Tue, 23 Dec 2025 11:15:52 GMT
• Title: AWPO: Enhancing Tool-Use of Large Language Models through Explicit Integration of Reasoning Rewards
• Authors: Zihan Lin, Xiaohan Wang, Hexiong Yang, Jiajun Chai, Jie Cao, Guojun Yin, Wei Lin, Ran He,
• Abstract summary: We propose advantage-weighted policy optimization (AWPO) to integrate explicit reasoning rewards to enhance tool-use capability.<n>AWPO incorporates variance-aware gating and difficulty-aware weighting to adaptively modulate advantages from reasoning signals.<n>Experiments demonstrate that AWPO achieves state-of-the-art performance across standard tool-use benchmarks.
• Score: 60.2998874976509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While reinforcement learning (RL) shows promise in training tool-use large language models (LLMs) using verifiable outcome rewards, existing methods largely overlook the potential of explicit reasoning rewards to bolster reasoning and tool utilization. Furthermore, natively combining reasoning and outcome rewards may yield suboptimal performance or conflict with the primary optimization objective. To address this, we propose advantage-weighted policy optimization (AWPO) -- a principled RL framework that effectively integrates explicit reasoning rewards to enhance tool-use capability. AWPO incorporates variance-aware gating and difficulty-aware weighting to adaptively modulate advantages from reasoning signals based on group-relative statistics, alongside a tailored clipping mechanism for stable optimization. Extensive experiments demonstrate that AWPO achieves state-of-the-art performance across standard tool-use benchmarks, significantly outperforming strong baselines and leading closed-source models in challenging multi-turn scenarios. Notably, with exceptional parameter efficiency, our 4B model surpasses Grok-4 by 16.0 percent in multi-turn accuracy while preserving generalization capability on the out-of-distribution MMLU-Pro benchmark.

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