Researchers from Tsinghua University and Microsoft Introduce ToRA: An Artificial Intelligence Tool-Integrated Reasoning Agent for Mathematical Problem Solving

Significant strides have been made in artificial intelligence and mathematical problem-solving, especially with the advent of large language models. However, these models still grapple with complex mathematical challenges. Microsoft and Tsinghua University researchers introduce TORA, a groundbreaking approach known as Tool-integrated Reasoning Agents, designed to tackle intricate mathematical problems by blending natural language reasoning with external computational tools.

Researchers have turned to integrating external tools like calculators, code interpreters, and symbolic solvers to address these challenges. While program-based methods have effectively transformed reasoning tasks into program synthesis tasks, they face nuanced reasoning, planning, and error-handling issues. Augmenting Large language models (LLMs) with these tools has significantly improved reasoning and generation performance. Knowledge distillation techniques, like LLM-generated trajectories for fine-tuning, have also played a role in transferring knowledge from teacher models to student models. 

LLMs have made notable strides in language tasks, including mathematical reasoning, yet complex mathematics remains challenging. Current strategies for enhancing mathematical prowess in LLMs involve step-by-step natural language reasoning and program synthesis. While the former excels in semantic and abstract reasoning, the latter thrives in rigorous operations and can tap into specialized tools like equation solvers. Their approach outperforms open-source models on mathematical reasoning datasets, achieving high accuracy, particularly on the competition-level MATHS dataset. Their method also offers insights into tool interaction’s advantages and unresolved challenges in mathematical reasoning, guiding future research in this domain.

TORA models were trained using interactive tool-use trajectories on mathematical datasets, applying imitation learning on the annotations and refining reasoning behavior with output space shaping. GPT-4 generated diverse reasoning patterns on training sets. Instructions and few-shot examples were composed in an interleaved format for prompt curation, and TORA’s effectiveness, which integrates rationales with programs, was evaluated. It achieved significant reasoning performance improvements. The challenges identified included a deeper understanding of geometric space and addressing complex symbolic reasoning in Intermediate Algebra and Precalculus problems.

TORA enhances mathematical reasoning by integrating natural language reasoning with external tools. TORA models excel on ten mathematical reasoning datasets, outperforming open-source models with 13%-19% absolute improvements on average and in program-based problem-solving. Their approach analyses tool interaction benefits and challenges, highlighting the effectiveness of TORA’s Tool-integrated Reasoning format, which interweaves rationales and program execution.

TORA represents a significant mathematical problem-solving advancement by seamlessly integrating natural language rationale with program-based tool use. It achieves state-of-the-art performance across various mathematical reasoning tasks, surpassing existing rationale and program-based approaches. The comprehensive analysis of tool interaction benefits and challenges offers critical insights for future research, promising to develop more advanced and adaptable reasoning agents.

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