Despite the impressive capabilities of Large Language Models (LLMs) on various tasks, they still struggle with scenarios that involves complex reasoning and planning. Recent work proposed advanced prompting techniques and the necessity of fine-tuning with high-quality data to augment LLMs' reasoning abilities. However, these approaches are inherently constrained by data availability and quality. In light of this, self-correction and self-learning emerge as viable solutions, employing strategies that allow LLMs to refine their outputs and learn from self-assessed rewards. Yet, the efficacy of LLMs in self-refining its response, particularly in complex reasoning and planning task, remains dubious. In this paper, we introduce AlphaLLM for the self-improvements of LLMs, which integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop, thereby enhancing the capabilities of LLMs without additional annotations. Drawing inspiration from the success of AlphaGo, AlphaLLM addresses the unique challenges of combining MCTS with LLM for self-improvement, including data scarcity, the vastness search spaces of language tasks, and the subjective nature of feedback in language tasks. AlphaLLM is comprised of prompt synthesis component, an efficient MCTS approach tailored for language tasks, and a trio of critic models for precise feedback. Our experimental results in mathematical reasoning tasks demonstrate that AlphaLLM significantly enhances the performance of LLMs without additional annotations, showing the potential for self-improvement in LLMs.

翻译：尽管大语言模型（LLMs）在各种任务上展现出令人印象深刻的能力，但它们仍难以应对涉及复杂推理与规划的场景。近期研究提出了先进的提示工程技术，并强调需使用高质量数据进行微调以增强LLMs的推理能力。然而，这些方法本质上受限于数据的可用性和质量。鉴于此，自我修正与自我学习成为可行的解决方案，其采用允许LLMs优化输出并从自我评估的奖励中学习的策略。然而，LLMs在自我完善其响应方面的有效性，尤其是在复杂推理与规划任务中，仍存疑义。本文提出AlphaLLM以实现LLMs的自我改进，该方法将蒙特卡洛树搜索（MCTS）与LLMs相结合，构建自我改进循环，从而无需额外标注即可增强LLMs的能力。受AlphaGo成功经验的启发，AlphaLLM解决了将MCTS与LLM相结合以实现自我改进的独特挑战，包括数据稀缺、语言任务搜索空间庞大以及语言任务中反馈的主观性。AlphaLLM由提示合成组件、针对语言任务定制的高效MCTS方法以及用于精准反馈的三元评判模型组成。我们在数学推理任务中的实验结果表明，AlphaLLM无需额外标注即可显著提升LLMs的性能，展现了LLMs自我改进的潜力。