Multi-Turn RL Training for Preference-Aligned Generative Models
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Solution Overview
Problem
Conventional fine-tuning methods for generative models in multi-turn tasks often overemphasize individual outputs, leading to less preferable multi-turn interactions, and are computationally expensive due to the need for interactive human feedback.
Innovation Solution
A method for training generative models using preference measures that compare complete interactions, optimizing an objective function based on actor-critic reinforcement learning, allowing for efficient fine-tuning without interactive human feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional fine-tuning methods are used for multi-turn tasks, then individual outputs can be optimized, but the overall multi-turn interaction quality deteriorates and computational costs increase
Solution Approach 1:
The patent segments the training process into separate components: a policy model that generates individual outputs and a critic model that evaluates complete multi-turn interactions. This allows independent optimization of individual output quality while simultaneously improving overall interaction quality through the critic's feedback on complete interaction sequences.
Solution Approach 2:
The patent implements a feedback mechanism where the critic model evaluates complete multi-turn interactions and provides feedback to the policy model. This feedback loop enables the system to learn from overall interaction quality rather than only individual outputs, resolving the contradiction between optimizing individual outputs and maintaining overall interaction quality.
2Reliability
If interactive human feedback is used for fine-tuning, then model alignment with human preferences is improved, but computational costs and complexity increase
Solution Approach 1:
The patent implements self-service by using the critic model to automatically evaluate interactions and provide feedback without requiring interactive human feedback. The critic model serves itself by generating evaluations based on learned preferences, reducing computational complexity while maintaining model alignment with human preferences through pre-trained evaluation criteria.
Solution Approach 2:
The patent creates a copy of the evaluation process through the critic model, which replicates human preference evaluation without requiring actual human interaction. This copy allows automated evaluation of multi-turn interactions, reducing computational complexity while maintaining alignment with human preferences through the critic's trained evaluation framework.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a generative machine learning model using multi-turn training examples that include sequences of example inputs and example outputs. In one aspect, a method comprises, at each of a sequence of training iterations: obtaining a plurality of example interactions, wherein each example interaction includes example model inputs and example model outputs for a plurality of time steps; obtaining one or more reference interactions for each example interaction, wherein each reference interaction includes reference model inputs and reference model outputs for a plurality of time steps; determining a preference measure for each example interaction based on a comparison between the example interaction and the reference interactions for the example interaction; and updating the target generative machine learning model to optimize an objective function that includes the preference measures for the plurality of example interactions.