Large language models (LLMs) have revolutionized how we interact with technology, but they still struggle with complex reasoning. Imagine trying to solve a multi-step math problem or strategizing a chess move – it takes more than just memorizing facts. It takes a structured approach. This is where the innovative "Buffer of Thoughts" (BoT) approach comes in. Researchers have devised a way to boost LLMs’ reasoning abilities by giving them a sort of mental scratchpad. BoT lets LLMs store and retrieve high-level "thought-templates" – blueprints for tackling various problem types. Think of it like having a toolbox of problem-solving strategies. When faced with a new problem, the LLM doesn't start from square one. It retrieves a relevant thought-template from its buffer and adapts it to the specifics of the current challenge. This reduces the computational burden and allows for faster, more accurate reasoning. The results? Impressive performance gains on challenging reasoning tasks like the Game of 24 and Checkmate-in-One. BoT helped LLMs improve accuracy by up to an astounding 79%. Even more compelling, smaller LLMs equipped with BoT begin to rival the performance of much larger, more resource-intensive models. This opens up exciting possibilities for running powerful AI on less powerful devices. BoT isn't just about solving puzzles, it's about building more efficient, and more robust reasoning capabilities into LLMs. This research signals a move towards making AI not just bigger, but smarter, paving the way for broader real-world applications. While still in its early stages, BoT and similar approaches hold immense potential for transforming how LLMs tackle complex reasoning. Challenges like human-like creativity and efficient template refinement remain. Yet, by learning from past solutions and adapting them to new situations, LLMs with 'Buffers of Thoughts' are taking a significant leap towards more human-like problem-solving abilities.