Implicit Reward Alignment for Stable Generative AI Fine-Tuning
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Solution Overview
Problem
Existing reinforcement learning techniques for training generative AI models, such as RLHF and DPO, are complex, time-consuming, unstable, and memory-intensive, and do not effectively utilize reward model information, leading to issues like overfitting and ethical biases.
Innovation Solution
UNA (UNified Alignment) unifies RLHF and DPO by mapping a reward model with a desired policy for generative AI models, performing supervised learning to minimize the difference between implicit and explicit rewards, accommodating various feedback types, and transforming the RL fine-tuning stage into a stable, efficient process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning techniques (RLHF, DPO) are used to train generative AI models, then the models can be aligned with desired policies, but the training process becomes complex, time-consuming, unstable, and memory-intensive
Solution Approach 1:
The patent combines the reward model training and policy optimization into a unified supervised learning framework. Instead of separate RLHF or DPO procedures, the system jointly optimizes both components through a single training objective that minimizes the difference between implicit and explicit rewards, thereby simplifying the overall training process while maintaining alignment quality.
Solution Approach 2:
The patent introduces an implicit reward model as an intermediary component that bridges the generative AI model and the explicit reward model. This implicit reward model learns to predict rewards without direct human feedback, enabling the system to utilize diverse feedback data types while reducing the complexity of direct reinforcement learning optimization.
2Reliability
If reinforcement learning techniques are used to train generative AI models, then alignment can be achieved, but the training process becomes time-consuming and unstable
Solution Approach 1:
The patent replaces the complex reinforcement learning optimization mechanism with a supervised learning approach. By formulating the alignment problem as a supervised classification task where the model learns to predict implicit rewards from feedback data, the system avoids the iterative and unstable nature of traditional RL optimization, significantly reducing training time and improving stability.
3Reliability
If reinforcement learning techniques are used to train generative AI models, then policy alignment can be achieved, but memory burden increases
Solution Approach 1:
The patent extracts and eliminates the need for complex reinforcement learning components such as value functions, baseline models, and extensive buffer storage. By using supervised learning with a simplified architecture that directly predicts implicit rewards from feedback data, the system reduces memory requirements while maintaining policy alignment capabilities.
4Measurement precision
If traditional reward models are used, then explicit feedback can be processed, but implicit reward information is not effectively utilized, leading to overfitting and ethical biases
Solution Approach 1:
The patent performs preliminary learning of implicit reward patterns through supervised training on diverse feedback data before final policy optimization. The implicit reward model is pre-trained to capture underlying reward structures from various feedback types, which then guides the policy learning process and prevents overfitting to specific explicit feedback patterns, thereby improving model robustness and reducing ethical biases.
Data Source
AI summary
A method may include obtaining a generative artificial intelligence (AI) model that includes a set of weights and that is associated with an explicit reward model and an implicit reward model. The method may include zeroing a partition function of the implicit reward model. The method may include obtaining feedback data associated with the explicit reward model that includes preference feedback data, binary feedback data, score feedback data, or any combination thereof. The method may include generating the explicit reward model based on the feedback data. The method may include fine-tuning the set of weights of the generative AI model based on a comparison of the explicit reward model and the implicit reward model and further based on the feedback data. The method may include receiving a query and generating, based on the query and the fine-tuned set of weights, a response that is responsive to the query.