Reinforcement learning through preference feedback
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Solution Overview
Problem
Conventional methods for training neural networks based on preferences are inefficient, susceptible to reward hacking, and struggle to accurately model non-transitive preferences, limiting their applicability and effectiveness in various applications.
Innovation Solution
The described systems utilize a pair-wise preference function to train neural networks by comparing outputs from a target neural network with alternative networks, incorporating a preference model to generate scores indicating higher quality outputs and updating weights to achieve a Nash equilibrium, while regularizing with a reference neural network to ensure desirable outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional reward model training is used, then training simplicity is maintained, but training efficiency is poor and reward hacking occurs
Solution Approach 1:
The patent introduces a preference model as an intermediary component that mediates between the reward model and the generator. This preference model processes pairs of outputs and determines preferences, thereby improving training efficiency and reducing reward hacking by adding a layer of preference-based evaluation rather than direct reward optimization
Solution Approach 2:
The patent implements a feedback mechanism where the preference model's outputs are used to update both the reward model and the generator through gradient descent. This feedback loop enables more efficient and reliable training by continuously adjusting models based on preference feedback, preventing reward hacking while improving productivity
2Adaptability or versatility
If conventional preference training methods are used, then implementation simplicity is maintained, but ability to model non-transitive preferences is poor
Solution Approach 1:
The patent segments the preference modeling task into distinct components: a preference model that processes output pairs, a reward model, and a generator. This segmentation allows the system to handle non-transitive preferences effectively by evaluating preferences in pairs rather than attempting to model global preference structures, managing complexity through modular design
3Measurement precision
If more training iterations are performed, then performance threshold is achieved, but training time and computational resources increase
Solution Approach 1:
The preference-based feedback mechanism provides more informative gradients that guide the generator and reward model more efficiently toward high-performance regions. This feedback quality accelerates convergence, enabling the system to achieve performance thresholds in fewer iterations and reducing training time while maintaining measurement precision
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks using a preference function that compares a measure of quality between network outputs. According to one aspect there is provided a method for training a target neural comprising: at each of a plurality of training steps, receiving one or more network inputs; for each of the network inputs, processing the network input using the target neural network to generate a first network output, processing the network input using an alternative neural network for the training step to generate a second network output, and applying a preference function to the first and second network outputs to generate a preference score comparing the first and second network outputs; and updating the target neural network weights using an objective function that encourages the first network outputs to be preferred over the second network outputs.