Summary

The research paper, "R ATIONALYST: Pre-training Process-Supervision for Improving Reasoning" presents an approach to improve the reasoning capabilities of large language models (LLMs) by addressing the challenge of incomplete reasoning steps, caused by lacking implicit rationales in their pre-training data. The proposed model, R ATIONALYST, is pre-trained on a vast collection of implicit rationales extracted from a mixture of web-scale unlabeled datasets and existing reasoning datasets.

R ATIONALYST is designed to provide process supervision for LLMs. It is able to consistently generalize across diverse reasoning tasks, including mathematical, commonsense, scientific, and logical reasoning, by consistently generating implicit rationales at inference time. With fine-tuning from LLaMa-3-8B, R ATIONALYST is shown to improve the accuracy of reasoning by an average of 3.9% on representative reasoning benchmarks. The paper compares R ATIONALYST's performance with significantly larger verifiers like GPT-4 and similarly-sized models fine-tuned on matching training sets, demonstrating its superiority in providing process supervision and improving reasoning capabilities. Additionally, the paper also discusses the potential benefits of supervising reasoning processes using explicit and implicit supervision methods.

The authors also provide extensive details about the rationale extraction, filtration, and training process, as well as the inference settings and evaluation tasks used to demonstrate the effectiveness of R ATIONALYST in enhancing the reasoning abilities of LLMs across various reasoning tasks. Furthermore, the paper outlines the potential implications of the research, such as the need for scaling up R ATIONALYST and its potential alignment with research on increasing test-time compute for language models. Finally, the authors acknowledge the limitations of their approach and suggest avenues for future work, such as expanding the range of reasoning tasks and adjusting the combination of rationales used to train R ATIONALYST.

The given article focuses on the use of rationales to enhance and guide the reasoning process in solving various problems. The examples provided demonstrate how rationales can be generated to enable a smart assistant to reason step by step through math word problems, commonsense reasoning problems, and text-based reasoning problems. The rationales are used to provide insight into the reasoning trajectory and guide the assistant in generating the correct responses.

The article highlights the importance of generating clear and logical rationales to improve the reasoning process and ensure accurate problem-solving. In the specific example of a math problem, the given question involves solving a set of equations to find the value of the product xy. The reasoning trajectory relies on rewriting the equations to express y in terms of x and then substituting the expression for y into the first equation. The next step involves simplifying the equation and solving for x, leading to the value x = 7. The trajectory then proceeds to substitute x = 7 back into y = 6x - 42 to find y, resulting in y = 0. Finally, the trajectory calculates the product xy, which equals 7 \* 0 = 0, leading to the conclusion that the value of the product xy is 0. The article emphasizes the importance of providing clear, logical, and accurate reasoning trajectories to guide the smart assistant in solving problems.

The use of step-by-step reasoning and the generation of comprehensive rationales are highlighted as key factors in ensuring the correctness and reasonableness of the responses. Additionally, the article discusses the assignment of rewards to reasoning trajectories based on their correctness and logical reasoning, with the goal of improving the overall problem-solving process.

The examples provided illustrate the application of rationales and the assessment of reasoning trajectories to enhance the performance of smart assistants in solving a variety of problems, including math word problems, commonsense reasoning problems, and text-based reasoning problems.

Reference: https://arxiv.org/abs/2410.01044