From Implicit Exploration to Structured Reasoning: Guideline and Refinement for LLM s (2025)

Abstract: Large language models (LLMs) have advanced general-purpose reasoning, showing strong performance across diverse tasks. However, existing methods often rely on implicit exploration, where the model follows stochastic and unguided reasoning paths—like walking without a map. This leads to unstable reasoning paths, lack of error correction, and limited learning from past experience. To address these issues, we propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement. First, we extract structured reasoning patterns from successful trajectories and reflective signals from failures. During inference, the model follows these guidelines step-by-step, with refinement applied after each step to correct errors and stabilize the reasoning process. Experiments on the Big-Bench Hard (BBH) benchmark show that our method consistently outperforms strong baselines across diverse reasoning tasks. Analysis reveals that stepwise execution, refinement, and experience-based learning improve stability and generalization. We further explore model collaboration during refinement, offering insights into cross-model interactions. Notably, structured reasoning guided by learned instructions matches or even surpasses knowledge distilled through SFT, highlighting its scalability and effectiveness.

## introduction large language models (llms) @xcite @xcite have demonstrated strong generalization across diverse domains, including mathematics @xcite , logical reasoning @xcite , language understanding @xcite , and specialized applications such as finance @xcite . while in-context learning (icl) enables flexible adaptation, llms continue to struggle with complex multi-step reasoning tasks-including planning, symbolic manipulation, * corresponding author. and structured decision-making-which are essential for many real-world applications. overcoming these limitations is critical for improving both the theoretical understanding and practical reliability of llm-based systems. recent work has made progress toward this goal. many researchers have explored advanced reasoning paradigms built on chain-of-thought (cot) @xcite @xcite prompting. react @xcite introduces a "thought-action-observation" cycle that enables interaction with the environment and self-evaluation. tree-of-thought (tot) @xcite represents reasoning as a search over tree-structured trajectories, encouraging exploration of multiple possibilities. beats @xcite and fot @xcite use mcts @xcite and voting to improve robustness. notably, few-shot cot and fot attempt to leverage prior examples to improve reasoning. although these approaches represent progress toward more structured reasoning, they still operate under an implicit reasoning paradigm: they leverage past examples in limited ways, lack global guidance, and offer no principled mechanism for error correction. nevertheless, implicit reasoning methods face three persistent challenges in complex multi-step tasks. first, models often struggle to learn from prior experience and extract reusable strategies-reasoning is executed from scratch without leveraging past successes. second, reasoning trajectories are frequently unstable. in the absence of a well-defined global structure, models tend to deviate from valid reasoning paths, leading to the compounding of early-stage errors. third, existing methods lack mechanisms for error recovery; once a reasoning path is taken, there is typically no reflection or correction. as illustrated in figure 1 , these limitations underscore the need to move from implicit exploration toward structured reasoning guided by learned strategies and iterative refinement. therefore, we propose a structured reasoning framework based on guideline extraction and stepwise refinement to address three key challenges in complex reasoning: unstable reasoning paths, lack of error correction, and limited use of learned experience. the framework consists of two stages. first, we extract structured reasoning patterns and key decision points from successful trajectories to form generalizable guidelines. in parallel, we analyze failed cases to identify typical error patterns, which serve as reflective signals. second, during inference, the model follows the extracted guideline step by step. after each step, a refinement module evaluates the intermediate output and applies targeted corrections. this procedure introduces global planning, enables error recovery, and incorporates experiential learning. we evaluate our approach on complex reasoning tasks from the big-bench hard (bbh) benchmark @xcite across multiple models. our framework consistently outperforms strong baselines-including cot, react, tot, beats and fot-achieving notable gains in both accuracy and stability. further analysis reveals that step-wise execution enhances reasoning coherence through explicit decomposition, refinement enables realtime error correction, and experience-based learning produces effective instructions that outperform implicit self-planning. we also investigate model collaboration in the refinement stage, providing insights into cross-model interactions and division of labor. additionally, inspired by prior work @xcite that frames icl as implicit optimization, we compare our method to supervised fine-tuning (sft) using the same model (llama-3.3-70b), and observe better generalization across tasks. these findings suggest that structured reasoning with guideline and refinement not only improves interpretability and reliability, but also holds promise for scalable deployment in more complex and practical real-world scenarios. our main contributions are: ## method we propose a structured reasoning framework that transitions from implicit exploration to explicit, guideline-driven reasoning. our method is built on two complementary components: guideline learning: automatically extracts stable reasoning patterns from correct examples and summarizes reflective signals from failure cases to form structured, step-wise guidelines. guided execution with refinement: leverages learned guidelines to conduct reasoning stepby-step during inference, with refinement applied to each intermediate result for enhanced robustness. ## conclusion we propose a structured reasoning framework that transitions from implicit exploration to explicit process modeling via guideline extraction and stepwise refinement. this approach enhances reasoning stability, supports error correction, and enables experience-based generalization. experiments on eight bbh tasks across multiple models show consistent improvements over strong baselines, including cot, react, tot, beats and fot. further analysis highlights the contributions of stepwise execution, iterative refinement, and learned guidance. we also explore inter-model collaboration in the refinement stage and show that our method performs competitively with, and in some cases outperforms, supervised fine-tuning (sft) approaches that distill knowledge from larger models-demonstrating strong scalability and practical potential for complex reasoning tasks.