Multi-Sample Preference Distillation for Single-Step Sequence Models
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Solution Overview
Problem
Existing sequence processing models, such as Large Language Models (LLMs), face challenges in aligning outputs with human preferences efficiently due to high computational costs associated with multi-sample preference sampling processes like Best-of-N sampling, which are resource-intensive and prone to issues like reward misspecification and reward hacking.
Innovation Solution
A distillation approach is employed to transfer the multi-sample preference sampling distribution into a student model, using distribution matching techniques to create a streamlined student model that mimics the benefits of multi-sample preference sampling with reduced computational overhead, allowing for a single generation step.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-sample preference sampling process (e.g., Best-of-N sampling) is used to align model outputs with human preferences, then alignment quality is improved, but computational cost increases significantly
Solution Approach 1:
The patent creates a student model that copies the sampling behavior of the reference model's multi-sample preference sampling process. Instead of performing actual multi-sample sampling during generation, the student model learns to directly produce samples that mimic the distribution of preferred samples, thereby achieving similar alignment quality without the computational overhead of generating and evaluating multiple samples.
Solution Approach 2:
The patent performs preliminary multi-sample preference sampling during the distillation training phase to create training data. The student model is trained on this pre-computed data, so that during actual generation, no multi-sample sampling is needed. This preliminary action transfers the computational burden from inference time to training time, resolving the contradiction between alignment quality and inference computational cost.
2Reliability
If multi-sample preference sampling process is applied to reference model, then preference alignment is improved, but process complexity increases
Solution Approach 1:
The student model copies the effective sampling distribution of the reference model's multi-sample preference sampling process. By learning to directly output samples from this distilled distribution, the complex multi-sample process is replaced with a simple single-sample generation process, maintaining preference alignment while reducing process complexity.
Solution Approach 2:
The patent extracts the essential sampling behavior and preference information from the complex multi-sample process into a simplified student model. This extraction removes the unnecessary complexity of generating, scoring, and selecting among multiple samples, while preserving the core functionality of producing preference-aligned outputs.
3Measurement precision
If distribution matching loss is used to train student model, then training precision is improved, but computational demands increase
Solution Approach 1:
The patent uses distribution matching loss that operates in the logit space rather than requiring full probability distribution computation. By working with logit differences and using Monte Carlo estimation techniques, the method achieves precise distribution matching while reducing computational demands through efficient approximation methods and gradient estimation techniques.
Data Source
AI summary
Provided are analytical expressions for multi-sample preference sampling distributions. A distillation training approach can be framed as a distribution matching problem with respect to one of these analytical expressions. The distribution matching problem can be solved using various algorithms. For example, a student model can be finetuned via policy distillation techniques. The resulting student model is therefore able to provide the benefits of the multi-sample preference sampling process, including its robustness and ability to align with human preferences, while significantly reducing the computational overhead at inference time.