ToolCPT: Improving Tool Utilization in LLM Agents via Continuous Pre-training

Autonomous agents powered by large language models (LLM-based agents) are capable of using off-the-shelf tools to interact with the environment, solve real-world problems, and boost work efficiency. However, current approaches to enhancing tool use for LLM-based agents primarily focus on post-training fine-tuning or testtime context extension. These methods overlook the fundamental tool knowledge acquisition during the early training phase, where models actually learn and internalize core knowledge representations, restricting model performance on out-of-distribution tool usage. To solve such a problem, we introduce enhancing tool knowledge for LLM-based agents during continuous pre-training (ToolCPT). We identify and bridge a key gap in current LLM training by shifting focus from tool-calling patterns to deep internalization of core toolknowledge representations. We begin by curating 5.1 million code artifacts from largescale, high-quality code repositories. These artifacts are selected based on a set of criteria that defines a usable "proxy agent tool", thereby forming a comprehensive agent tool library. For each proxy tool, we then create a detailed playbook covering implementation specifications, core functionalities, interaction protocols with other tools, and illustrative positive and negative examples. This process yields a large-scale tool knowledge corpus comprising 18 billion tokens, which is used to continuously pre-train our model. Experiments show our playbook-enhanced corpus catalyzes deep knowledge internalization, driving the model to notable performance gains on multiple standard benchmarks.