Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch

• URL: http://arxiv.org/abs/2511.01934v2
• Date: Mon, 10 Nov 2025 16:36:41 GMT
• Title: Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch
• Authors: Yirong Zeng, Xiao Ding, Yutai Hou, Yuxian Wang, Li Du, Juyi Dai, Qiuyang Ding, Duyu Tang, Dandan Tu, Weiwen Liu, Bing Qin, Ting Liu,
• Abstract summary: Training tool-augmented language models has emerged as a promising approach to enhancing their capabilities for complex tasks.<n>We propose a dynamic generalization-guided reward design for rule-based reinforcement learning.<n>We show that our models achieve over 7% performance improvement compared to both SFT and RL-with-SFT models.
• Score: 63.40752011615843
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Training tool-augmented LLMs has emerged as a promising approach to enhancing language models' capabilities for complex tasks. The current supervised fine-tuning paradigm relies on constructing extensive domain-specific datasets to train models. However, this approach often struggles to generalize effectively to unfamiliar or intricate tool-use scenarios. Recently, reinforcement learning (RL) paradigm can endow LLMs with superior reasoning and generalization abilities. In this work, we address a key question: Can the pure RL be used to effectively elicit a model's intrinsic reasoning capabilities and enhance the tool-agnostic generalization? We propose a dynamic generalization-guided reward design for rule-based RL, which progressively shifts rewards from exploratory to exploitative tool-use patterns. Based on this design, we introduce the Tool-Zero series models. These models are trained to enable LLMs to autonomously utilize general tools by directly scaling up RL from Zero models (i.e., base models without post-training). Experimental results demonstrate that our models achieve over 7% performance improvement compared to both SFT and RL-with-SFT models under the same experimental settings. These gains are consistently replicated across cross-dataset and intra-dataset evaluations, validating the effectiveness and robustness of our methods.

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