LLM-First Search: Self-Guided Exploration of the Solution Space (2506.05213v1)

Abstract: LLMs have demonstrated remarkable improvements in reasoning and planning through increased test-time compute, often by framing problem-solving as a search process. While methods like Monte Carlo Tree Search (MCTS) have proven effective in some domains, their reliance on fixed exploration hyperparameters limits their adaptability across tasks of varying difficulty, rendering them impractical or expensive in certain settings. In this paper, we propose \textbf{LLM-First Search (LFS)}, a novel \textit{LLM Self-Guided Search} method that removes the need for pre-defined search strategies by empowering the LLM to autonomously control the search process via self-guided exploration. Rather than relying on external heuristics or hardcoded policies, the LLM evaluates whether to pursue the current search path or explore alternative branches based on its internal scoring mechanisms. This enables more flexible and context-sensitive reasoning without requiring manual tuning or task-specific adaptation. We evaluate LFS on Countdown and Sudoku against three classic widely-used search algorithms, Tree-of-Thoughts' Breadth First Search (ToT-BFS), Best First Search (BestFS), and MCTS, each of which have been used to achieve SotA results on a range of challenging reasoning tasks. We found that LFS (1) performs better on more challenging tasks without additional tuning, (2) is more computationally efficient compared to the other methods, especially when powered by a stronger model, (3) scales better with stronger models, due to its LLM-First design, and (4) scales better with increased compute budget. Our code is publicly available at \href{https://github.com/NathanHerr/LLM-First-Search}{LLM-First-Search}.

Overview of "LLM-First Search: Self-Guided Exploration of the Solution Space"

The paper "LLM-First Search: Self-Guided Exploration of the Solution Space" introduces a novel search methodology termed LLM-First Search (LFS), rooted in the capabilities of LLMs to autonomously guide the search process without predefined heuristics or hyperparameter-driven strategies. This approach is positioned to overcome the limitations of classical search techniques, such as Monte Carlo Tree Search (MCTS), Best First Search (BestFS), and Breadth First Search (BFS), which rely heavily on fixed exploration constants and hardcoded policies, often making them cumbersome in varying task environments.

Methodology and Contributions

The core innovation of LFS lies in its self-guided mechanism, where the LLM dynamically evaluates whether to continue exploring the current search path or divert to alternative branches based on internal scoring mechanisms. This dynamic decision-making process is achieved without manual tuning or task-specific adaptations, which are characteristic of traditional search algorithms.

The authors evaluate LFS using two reasoning task benchmarks: Countdown and Sudoku. These tasks serve as testbeds to compare LFS against established search algorithms, namely ToT-BFS, BestFS, and MCTS. The key findings reveal:

1. Adaptability and Performance: LFS demonstrates superior adaptability and performance in tackling complex tasks without additional tuning, providing a flexible framework applicable across various problem domains.
2. Computational Efficiency: LFS exhibits enhanced computational efficiency, especially when integrated with stronger LLMs, suggesting that the LLM-First design scales effectively with increased model capacity and compute budget.
3. Scalability: The method shows improved performance scalability with the strengthening of the underlying LLM, outperforming traditional methods as the model size increases.

Numerical Results and Analysis

In quantitative evaluations, LFS consistently outperforms other tested methods in terms of average WinRate and EfficiencyScore. For example, in more challenging setups, such as Countdown with higher difficulty levels, LFS records a higher WinRate, marking significant improvements over MCTS, particularly in scenarios with limited token budgets. Similarly, LFS maintains computational efficiency while achieving competitive performance, thereby underscoring its capability to solve reasoning tasks more effectively than conventional search algorithms.

Implications and Future Directions

The implications of this research are multifaceted, extending to both practical applications and theoretical exploration. Practically, LFS can be employed in diverse domains requiring complex decision-making and planning, where adaptability and efficiency are critical. Theoretically, the deployment of LLMs in guiding search processes suggests pathways for further integration of AI models with autonomous reasoning capabilities.

Future developments could explore the integration of LFS with additional prompting strategies like reflection and debate frameworks, which could further enhance performance across varied task complexities. Extending the evaluation to more realistic settings and tasks beyond standard benchmarks could also reveal additional strengths and limitations of LFS.

In conclusion, this paper presents LFS as an effective alternative to classic search methodologies by leveraging the intrinsic capabilities of LLMs to self-guide exploration, offering enhanced adaptability, scalability, and efficiency in solving complex reasoning tasks. This approach redefines how AI models can autonomously manage reasoning processes, paving the way for more integrated and human-like problem-solving systems.