Abstract
Parametric retrieval enables LLMs to implicitly retrieve tools by assigning each API a unique virtual token and training the model to generate it via constrained beam search. Toolsense identifies two critical drawbacks: it destroys parametric tool knowledge during training and its beam-search decoding is too slow for real-time deployment.
We introduce TRACE (Tool Retrieval via Augmented Chain-of-thought and Enterprise rules), a two-stage curriculum that resolves this dissociation. Stage 1 reuses the multi-format memorization SFT from ToolSense to seed tool knowledge with LoRA. Stage 2 is our core contribution: the model is trained to emit a thinking trace before producing a JSON list of tool tokens, using two data sources -- RRB pairs from ToolSense and queries synthesized to target business rules curated by domain experts -- both augmented with reasoning traces. This training objective preserves Stage 1 MCQ and QA probing accuracy while enabling single-beam greedy decoding at production latency.
Evaluated on a combined enterprise catalog of over 8,300 tools across two enterprise product lines, TRACE training for Stage 2 not only preserves but improves tool understanding: MCQ accuracy gains +3.2 pp and QA probing gains +9 pp over Stage 1. On retrieval, TRACE achieves ~86% recall on Domain A and ~60% on Domain B -- compared to embedding baseline performance of ~27% & ~52% -- both with single-beam greedy decoding, making it directly deployable at production latency.
Blogger's Review: TRACE's innovative approach of integrating augmented reasoning with business rules significantly enhances the efficiency and accuracy of parametric tool retrieval, providing a robust foundation for practical applications of large language models while ensuring knowledge retention and faster decoding speeds.