Low-Rank Generative Model Training for Multi-Domain Response Quality
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Solution Overview
Problem
Fine-tuning generative models is computationally expensive due to their large number of parameters, and existing techniques often limit models to a single or small number of domains, relying on small, labeled datasets.
Innovation Solution
Implement low-rank representations of pre-trained generative models, utilizing a decoupled reward model for reinforcement learning, which reduces the number of parameters to be trained, thereby reducing computational resources and maintaining general-purpose capabilities across multiple domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full fine-tuning of pre-trained GM is performed, then model response quality to user prompts is improved, but computational resource expenditure increases significantly
Solution Approach 1:
The patent segments the model parameters into two distinct groups: frozen pre-trained parameters and trainable low-rank decomposition parameters. This segmentation allows the model to maintain high response quality through the frozen pre-trained components while significantly reducing computational resource expenditure by training only the small subset of low-rank parameters (e.g., 0.01% to 0.1% of total parameters) during fine-tuning.
Solution Approach 2:
The patent extracts and isolates the essential trainable components from the full model by representing weight updates as low-rank decompositions. Instead of training all parameters, only the low-rank factors are extracted and trained, while the majority of parameters remain frozen, thereby reducing computational burden while preserving model performance.
2Use of energy by moving object
If low-rank training with small labeled datasets is used, then computational resource expenditure is reduced, but model capabilities are limited to single or small number of domains
Solution Approach 1:
The patent implements a universal low-rank adaptation framework that can be applied across multiple domains and task types. The same low-rank decomposition mechanism serves as a general-purpose fine-tuning approach that maintains domain-agnostic capabilities while adapting to specific tasks, enabling the model to handle diverse domains (e.g., NLP, vision, audio) without retraining the entire model.
Solution Approach 2:
The patent changes the parameter representation from full-rank weight matrices to low-rank decompositions, fundamentally altering how model adaptations are stored and computed. This parameter transformation enables efficient storage and training while preserving the model's ability to generalize across domains through the compact low-rank representation.
3Quantity of substance
If low-rank representation is applied, then number of trainable parameters is reduced, but training speed and convergence may be affected
Solution Approach 1:
The patent transforms the parameter space by applying low-rank decomposition to the weight update matrices. This parameter change reduces the number of trainable parameters from billions to thousands or millions, while the gradient computation and parameter updates are optimized to maintain training speed, achieving faster convergence due to the reduced parameter space.
Solution Approach 2:
The patent segments the parameter update process into low-rank factor computation and application stages. This segmentation allows efficient computation where gradients are computed for the full model but applied only to the low-rank factors, improving training speed by reducing the computational burden of parameter updates while maintaining full model gradient information.
Data Source
AI summary
Some implementations relate to receiving input data; generating, using a low-rank representation of a machine-learned generative model, a generative output from the input data; determining, based on a machine-learned reward model, a corresponding reward from the generative output, and updating, based on the corresponding reward, one or more parameters of the low-rank representation of the machine-learned model. Further, some additional or alternative implementations relate to receiving input data associated with a client device; generating, using a general purpose agent, responsive content to the input data, wherein the general purpose agent is configured based on a machine-learned generative model and a low-rank representation of the machine-learned generative model; and causing the client device to render the responsive content.