Neural Network Alignment Using Reward Models and Forward KL
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for aligning neural network-based models, such as large language models (LLMs), are slow and expensive due to the reliance on reinforcement learning with human feedback (RLHF), which requires significant manual labor and computational resources, and face challenges like mode collapsing and non-differentiability in optimizing reverse Kullback-Leibler divergence.
Innovation Solution
A generation framework that fine-tunes LLMs by generating multiple candidate responses, evaluating reward scores, and minimizing forward Kullback-Leibler divergence using a residual energy-based model, allowing for efficient alignment without direct human feedback through methods like Direct Preference Optimization (DPO) and self-normalized importance sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning with human feedback (RLHF) is used for fine-tuning LLMs, then alignment with human preferences is achieved, but training time and computational cost increase significantly
Solution Approach 1:
The patent introduces a reward model as an intermediary between human feedback and the language model training. Instead of directly using slow human feedback for gradient computation, the reward model quickly evaluates candidate responses and provides differentiable reward signals, enabling efficient reinforcement learning while maintaining alignment quality
Solution Approach 2:
The patent replaces the non-differentiable sampling operation in traditional RLHF with a differentiable Gumbel-Softmax approximation. This substitution enables gradient flow through the sampling process, allowing end-to-end optimization and significantly improving training efficiency while maintaining the ability to learn from human preferences
2Reliability
If reinforcement learning with human feedback (RLHF) is used for fine-tuning LLMs, then alignment with human preferences is achieved, but manual labor requirements increase significantly
Solution Approach 1:
The patent performs preliminary action by collecting human feedback offline to train a reward model before the actual fine-tuning process. This pre-trained reward model can then efficiently evaluate responses during training without requiring real-time human involvement, dramatically reducing manual labor costs while preserving alignment quality
Solution Approach 2:
The patent creates a copy of human preferences in the form of a trained reward model that mimics human evaluation behavior. This computational copy can rapidly assess responses without requiring actual human annotators, reducing manual labor while maintaining the essence of human-preferred alignments
3Reliability
If reverse Kullback-Leibler divergence optimization is used, then alignment objective is formulated, but mode collapsing and non-differentiability issues occur
Solution Approach 1:
The patent inverts the traditional reverse KL divergence formulation by using forward KL divergence instead. This inversion changes the optimization objective in a way that naturally avoids mode collapsing and enables the use of Gumbel-Softmax for differentiable sampling, simplifying the optimization process while maintaining alignment effectiveness
Data Source
AI summary
Embodiments described herein provide A method of fine-tuning a neural network based model. In some embodiments, a system receives, via a data interface, a training dataset including a plurality of input samples. The system generates, via a pre-trained neural network based model, a first response based on a first input sample of the plurality of input samples, and a second response based on the first input sample. The system generates, via a trained reward model, a first reward score based on the first input sample and the first response, and a second reward score based on the first input sample and the second response. The system computes a loss function based on the first prompt, the first response, the second response, the first reward score, and the second reward score. The system updates parameters of the neural network based model based on the loss function.