Sequence Model Training via Intermediate Reasoning Traces
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Solution Overview
Problem
Machine-learned models lack the ability to effectively process and understand complex queries that require intermediate logical steps, relying solely on raw answers without learning from human reasoning processes.
Innovation Solution
The method involves training machine-learned sequence processing models using datasets with ground-truth chain-of-thought reasoning traces, which provide step-by-step explanations, enabling the models to learn and generate intermediate reasoning steps alongside answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If machine-learned models are trained using only raw answers without intermediate reasoning steps, then training data requirements are reduced and training process is simpler, but the models cannot effectively process complex queries requiring intermediate logical steps
Solution Approach 1:
The patent segments the training data into multiple components: queries, intermediate reasoning steps (traces), and final answers. This segmentation allows the model to learn and process complex queries step-by-step through intermediate reasoning, improving its ability to handle complex tasks while maintaining a manageable training structure
Solution Approach 2:
The patent incorporates preliminary reasoning traces in the training data that show the step-by-step thought process before reaching the final answer. This preliminary action in the training data enables the model to learn and replicate effective reasoning processes, improving its capability to process complex queries
2Productivity
If machine-learned models rely solely on raw answers, then training is faster and requires less computational resources, but the models lack interpretability and cannot provide comprehensible explanations
Solution Approach 1:
The patent introduces intermediate reasoning traces as intermediary elements between queries and final answers in the training data. These traces serve as mediators that explain the reasoning process, enabling the model to provide comprehensible explanations while maintaining efficient training through automated evaluation of both traces and answers
3Measurement precision
If ground truth traces are obtained from human annotations, then the quality of reasoning traces is improved, but the data collection process becomes more time-consuming and costly
Solution Approach 1:
The patent uses automated evaluation systems that provide feedback on the quality of generated traces and answers. This feedback mechanism allows continuous improvement of trace quality through automated metrics while reducing reliance on time-consuming human annotation for every detail, balancing quality with efficiency
Data Source
AI summary
An example method for training a machine-learned sequence processing model includes obtaining a plurality of training examples for training the machine-learned sequence processing model. For each respective training example of the plurality of training examples, the example method includes: obtaining a respective query associated with the respective training example; inputting the respective query to the machine-learned sequence processing model; obtaining, from the machine-learned sequence processing model a response to the respective query and a trace of intermediate states from the respective query to the response; evaluating the response using a ground truth response associated with the respective training example; evaluating the trace using a ground truth trace associated with the respective training example; and updating one or more parameters of the machine-learned sequence processing model based on the evaluation of the response and based on the evaluation of the trace.