Reason from Future: Reverse Thought Chain Enhances LLM Reasoning (2025)

Abstract: It has been demonstrated that carefully designed reasoning paradigms, like Chain-of-Thought(CoT) and Tree-of-Thought(ToT), can enhance the reasoning capabilities of small language models by detailed thinking and extensive thought searching, unbounded branching factors in the searching space create prohibitive reasoning consumption. However these methods fell into the trap of local optimum reasoning, which means the model lacks a global perspective while solving problems. We propose a novel reasoning paradigm called Reason from Future(RFF), which generates reasoning paths by bidirectional reasoning that combines top-down planning with bottom-up reasoning accumulation. The essence of RFF lies in its reverse reasoning mechanism, which prioritizes core logical relationships and imposes goal-oriented constraints on intermediate steps, thereby reducing the searching space and mitigating error accumulation inherent in sequential forward reasoning. Empirical evaluations across diverse experiments demonstrate that RFF outperforms conventional paradigms with higher accuracy and less searching space to solve complex tasks.

## introduction the rapid evolution of large language models (llms), fueled by breakthroughs in deep learning architectures and unprecedented datasets, has demonstrated remarkable potential across natural language processing (nlp) and interdisciplinary applications @xcite @xcite . llms like chatgpt @xcite and llama @xcite exhibit human-like text generation, multilingual task execution, and emerging logical reasoning. current scholarly investigations identify their reasoning capacity for problem * corresponding authors. try to minus bananas by 5 try to do apples + bananas to get 20 i see, do apples + bananas to get 20 the number of fruits is apples + bananas question: there are 10 apples and 10 bananas in the blanket, 5 bananas are a little smaller than others. how many fruits in the blanket? ## methods reason from future (rff) is a reasoning paradigm that allows models to solve a question by using forward and backward reasoning alternately. we use p θ to denote a llm with parameters p θ , and x, t to denote the input and question. the {s} ∼ {s 0 , s 1 ...s i }, {t } ∼ {t 0 , t 1 ...t i } denote the list of current states and the list of target states in each step i. we define o(p θ , x, t|s i ) as the output of the llm with parameters p θ using a prompt consisting of input x, target t, and hints s i . in the ith step, the model identifies the preceding step closest to the current target state t i-1 and considers it as the new target state t i and provides the calculation relationship between the two. then the model takes the t i-1 as the new target for one-step forward reasoning. the model then repeats this step until the latest target state has been achieved (s @xmath0. a specific rff pipeline should consist of three components: 1: last step generator g(); 2: stepwise forward reason r(); 3: state check c(). ## experiment we evaluate the effectiveness of rff on some widely used llm reasoning benchmarks, like game of 24 and gsm8k. considering that successful paradigm may be due to the strength of the model itself rather than the strength of the paradigm, leading to difficulty in migrating them to weak models or small models, we carry out our experiments using llama3-8b-instruct @xcite and qwen2.5-7b-instruct @xcite as the base models, and more detailed parameters will be shown in specific tasks setup. ## conclusion in this paper, we introduce reason from future (rff), a novel reasoning paradigm aiming at enhancing the reasoning ability of llms for complex problems. rff leverages a bidirectional reasoning framework integrates top-down planning with bottom-up reasoning accumulation to generate a solution path. this aids in the convergence of the search space for the model, thereby enhancing inference efficiency. simultaneously, it allows the model to focus on critical information, which improves the accuracy of reasoning. rff has demonstrated superior performance across both searching tree tasks (game of 24) and directed acyclic graph tasks (math problems and commonsense problems), showing the potential to enhance the model's reasoning capabilities.