ToolComp: A Multi-Tool Reasoning & Process Supervision Benchmark
This addresses the problem of evaluating and improving complex tool-use reasoning for AI developers, though it is incremental as it builds on existing process supervision concepts.
The paper tackles the challenge of evaluating multi-step tool-use reasoning in AI systems by introducing ToolComp, a benchmark with human-verified prompts and process supervision labels, where most models achieve less than 50% accuracy. It shows that process-supervised reward models (PRMs) outperform outcome-supervised ones, improving rank@1 accuracy by 19% and 11% for ranking model trajectories.
Despite recent advances in AI, the development of systems capable of executing complex, multi-step reasoning tasks involving multiple tools remains a significant challenge. Current benchmarks fall short in capturing the real-world complexity of tool-use reasoning, where verifying the correctness of not only the final answer but also the intermediate steps is important for evaluation, development, and identifying failures during inference time. To bridge this gap, we introduce ToolComp, a comprehensive benchmark designed to evaluate multi-step tool-use reasoning. ToolComp is developed through a collaboration between models and human annotators, featuring human-edited/verified prompts, final answers, and process supervision labels, allowing for the evaluation of both final outcomes and intermediate reasoning. Evaluation across six different model families demonstrates the challenging nature of our dataset, with the majority of models achieving less than 50% accuracy. Additionally, we generate synthetic training data to compare the performance of outcome-supervised reward models (ORMs) with process-supervised reward models (PRMs) to assess their ability to improve complex tool-use reasoning as evaluated by ToolComp. Our results show that PRMs generalize significantly better than ORMs, achieving a 19% and 11% improvement in rank@1 accuracy for ranking base and fine-tuned model trajectories, respectively. These findings highlight the critical role of process supervision in both the evaluation and training of AI models, paving the way for more robust and capable systems in complex, multi-step tool-use tasks.