DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search (2410.03864v1)

Abstract: Enhancing the capability of LLMs in reasoning has gained significant attention in recent years. Previous studies have demonstrated the effectiveness of various prompting strategies in aiding LLMs in reasoning (called "reasoning actions"), such as step-by-step thinking, reflecting before answering, solving with programs, and their combinations. However, these approaches often applied static, predefined reasoning actions uniformly to all questions, without considering the specific characteristics of each question or the capability of the task-solving LLM. In this paper, we propose DOTS, an approach enabling LLMs to reason dynamically via optimal reasoning trajectory search, tailored to the specific characteristics of each question and the inherent capability of the task-solving LLM. Our approach involves three key steps: i) defining atomic reasoning action modules that can be composed into various reasoning action trajectories; ii) searching for the optimal action trajectory for each training question through iterative exploration and evaluation for the specific task-solving LLM; and iii) using the collected optimal trajectories to train an LLM to plan for the reasoning trajectories of unseen questions. In particular, we propose two learning paradigms, i.e., fine-tuning an external LLM as a planner to guide the task-solving LLM, or directly fine-tuning the task-solving LLM with an internalized capability for reasoning actions planning. Our experiments across eight reasoning tasks show that our method consistently outperforms static reasoning techniques and the vanilla instruction tuning approach. Further analysis reveals that our method enables LLMs to adjust their computation based on problem complexity, allocating deeper thinking and reasoning to harder problems.

• The paper introduces DOTS, which dynamically adapts reasoning strategies in LLMs to improve task-specific accuracy.
• It leverages atomic reasoning action modules to customize response pathways for varying problem complexities.
• Extensive evaluations show that DOTS surpasses static prompting by enhancing performance on mathematical, common-sense, and symbolic reasoning tasks.

An Overview of "Dots: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search"

The paper "Dots: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search" introduces a method aimed at enhancing the reasoning capabilities of LLMs by tailoring reasoning techniques to the specific characteristics of each question and the intrinsic capabilities of the LLMs themselves. The approach, titled Dots, stands for Dynamic reasoning via Optimal Trajectories Search, which introduces a flexible mechanism for planning and adapting reasoning strategies.

Key Concepts and Methodology

The essence of the proposed method lies in its strategic formation of reasoning actions, a departure from static and uniform prompting techniques typically applied across all questions. The authors identify three essential steps:

1. Atomic Reasoning Action Modules: These are foundational components that form the building blocks of diverse reasoning trajectories. The modules include actions like query rewriting, decomposition, different reasoning formats such as Chain-of-Thought (CoT) and Program-of-Thought (PoT), and self-verification.
2. Optimal Reasoning Trajectory Search: This dynamic adaptation process involves exploring and evaluating various reasoning pathways for each question. It directly targets optimizing success rates and includes iterative exploration, making it a data-driven selection mechanism.
3. Trajectory Planning through Fine-Tuning: Either an external LLM (acting as a planner) is fine-tuned to guide the primary LLM, or the task-solving LLM internalizes this capability, adapting autonomously to unseen questions. This dual-setup approach allows Dots to capitalize on both closed-source or costly LLMs and open-source models.

Experimental Evaluation

The authors conduct a rigorous evaluation across numerous datasets covering in-distribution, few-shot, and out-of-distribution scenarios. Dots displays consistent superiority over static reasoning methods and other advanced prompt engineering solutions like chain-of-thought and program-guided reasoning. Specifically, in comprehensive testing across various tasks—spanning mathematical, common-sense, and symbolic reasoning—Dots showed enhanced performance metrics, affirming its adaptability and robust accuracy.

A notable aspect of the paper is its detailed analysis of reasoning action distributions, demonstrating that Dots effectively improves computation allocation based on problem complexity. The model is fine-tuned to employ deeper reasoning strategies for more complex questions, thereby acknowledging the inherent capabilities and limitations of the task-solving LLMs.

Implications and Future Directions

The implications of this research extend across both practical and theoretical domains. Practically, Dots provides a scalable methodology for improving the reasoning quality of LLMs. Theoretically, it supports the notion that reasoning isn't a one-size-fits-all process, expanding on the adaptability concept and aligning it closer with human-like reasoning progression.

Looking forward, possible developments include refining the granularity of reasoning action modules and further enhancing the efficiency of trajectory searches. Additionally, exploring integration with multi-modal models or real-time adaptability in evolving contexts could be fruitful pathways.

In conclusion, the Dots methodology offers a pathway toward more intelligent and context-aware reasoning processes in LLMs, marking a significant step towards maximizing their potential across diverse reasoning tasks. By allowing LLMs to autonomously determine and adapt their reasoning pathways, this approach sets a new direction in the dynamic reasoning capabilities of AI models.