Dialogue Model Training With Reward-Guided Joint Loss
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Solution Overview
Problem
Existing dialog models struggle to effectively integrate supervised fine-tuning and reinforcement learning to improve factual accuracy and user intention understanding in task-oriented dialog generation.
Innovation Solution
A training method that combines supervised fine-tuning and reinforcement learning by introducing a comprehensive loss function, utilizing a reward model to adjust dialog model parameters based on user satisfaction scores, ensuring joint optimization and improved user preference prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If supervised fine-tuning is used to train the dialog model, then the model can learn basic dialog generation capabilities, but the model lacks the ability to accurately predict user preferences and understand user intentions
Solution Approach 1:
The patent combines supervised fine-tuning and reinforcement learning into a unified training framework. The supervised fine-tuning provides foundational dialog generation capabilities while reinforcement learning with a reward model enhances user preference prediction. The joint training process integrates both objectives through a comprehensive loss function that balances task completion accuracy and user preference alignment.
Solution Approach 2:
The patent introduces a reward model as an intermediary component that evaluates dialog responses based on user satisfaction. This reward model acts as a mediator between the dialog model and the training objective, providing feedback signals that guide the dialog model to generate responses that better align with user preferences without requiring direct access to ground truth user preferences.
2Measurement precision
If reinforcement learning is used to improve user preference prediction, then the model can better understand user intentions, but the training process becomes more complex and difficult to implement
Solution Approach 1:
The patent performs preliminary training of the reward model using a dataset with annotated user preferences before using it in the reinforcement learning phase. This preliminary action prepares the reward model to provide accurate feedback signals, simplifying the subsequent reinforcement learning process by having a ready-to-use evaluation mechanism rather than developing the feedback system from scratch during training.
Solution Approach 2:
The patent implements a feedback mechanism where the reward model evaluates dialog responses and provides score signals back to the dialog model. This feedback loop enables the dialog model to iteratively improve its responses based on user preference evaluations, with the feedback serving as a guiding signal that simplifies the optimization process compared to trial-and-error approaches.
3Reliability
If a comprehensive loss function integrating both supervised and reinforcement learning objectives is used, then joint optimization can be achieved, but the loss calculation and parameter adjustment become more complex
Solution Approach 1:
The patent segments the comprehensive loss function into distinct components: a supervised learning loss term that measures task completion accuracy and a reinforcement learning loss term that measures user preference alignment. By segmenting the loss function, the patent enables separate optimization of each objective while maintaining their integration through weighted combination, making the overall optimization process more manageable and interpretable.
Data Source
AI summary
A method is provided. The method includes: obtaining a first sample dataset; inputting at least one first question text corresponding to at least one piece of first sample data into a dialog model separately to obtain at least one first answer prediction result; inputting each second question text into the dialog model to obtain a second answer prediction result output by the dialog model; inputting the second answer prediction result into a reward model to obtain a score of the second answer prediction result output by the reward model; determining a comprehensive loss based on the at least one first answer prediction result, a first answer text of each of the at least one piece of first sample data, and a score corresponding to each of at least one piece of second sample data; and adjusting at least one parameter of the dialog model based on the comprehensive loss.