Multiscale Refinement Objective for Student Model Knowledge Distillation
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Solution Overview
Problem
Existing machine learning models face challenges in efficiently distilling knowledge from larger teacher models to smaller student models, often resulting in suboptimal performance due to output distribution misalignment.
Innovation Solution
The proposed method involves a computer-implemented method for training a machine-learned student sequence processing model using a multiscale refinement objective. This objective combines a divergence metric characterizing the comparison of predictions between the student and teacher models with a reinforcement learning signal, allowing for joint distillation and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If knowledge distillation is performed from teacher models to student models, then computational cost is reduced, but performance deteriorates due to output distribution misalignment
Solution Approach 1:
The patent implements a feedback mechanism where the student model's output distribution is continuously compared with the teacher model's output distribution using a divergence metric. The feedback signal generated from this comparison is used to update the student model's parameters, enabling the student model to gradually align its output distribution with the teacher model's distribution while maintaining reduced computational cost.
Solution Approach 2:
The patent changes the parameters of the student model iteratively based on the feedback signal. By updating the student model parameters in the direction that reduces the divergence between student and teacher output distributions, the system achieves performance improvement while maintaining the computational efficiency benefits of using a smaller student model.
2Productivity
If a multiscale refinement objective combining divergence metric and reinforcement learning signal is used, then training efficiency is improved, but model complexity increases
Solution Approach 1:
The patent merges two training objectives into a single multiscale refinement objective: knowledge distillation (via divergence metric) and reinforcement learning (via reward signal). By combining these objectives, the student model simultaneously learns from the teacher model's output distribution and optimizes for task performance, improving training efficiency without requiring separate training stages or multiple models.
Solution Approach 2:
The patent segments the training objective into two components: a divergence metric component that measures the difference between student and teacher output distributions, and a reinforcement learning component that measures task performance. This segmentation allows each component to be optimized independently while contributing to the overall training process, making the complex objective manageable and efficient.
Data Source
AI summary
An example method is provided for training a machine-learned student sequence processing model, the method comprising: obtaining a respective input; obtaining, from the student machine-learned sequence processing model, a respective output corresponding to the respective input; generating a multiscale refinement objective configured to jointly distill knowledge from a teacher machine-learned sequence processing model and reinforce preferred behavior of the student machine-learned sequence processing model, wherein the multiscale refinement objective comprises: a first component based on a divergence metric characterizing, for the respective input, a comparison of a plurality of predictions of the student machine-learned sequence processing model to a plurality of predictions of the teacher machine-learned sequence processing model; and a second component based on a reinforcement learning signal associated with the respective output; and updating the machine-learned student sequence processing model based on the multiscale refinement objective.