Direct Posterior Preference Fine-Tuning Without Decode-Time Inference
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Solution Overview
Problem
Existing methods for fine-tuning sequence processing models like LLMs and LMMs are resource-intensive and inflexible, requiring complex reinforcement learning loops and additional inference steps, often lacking suitable datasets and overfitting to reward models, which limits their ability to adapt to diverse preference labels and computational efficiency.
Innovation Solution
Direct Posterior Preference Fine-Tuning (DPPFT) directly predicts posterior token probabilities conditioned on positive preferences, using architectures like Low Rank Adaptation and prefix prompt tuning to efficiently align model outputs with specific tasks or preferences without additional inference, reducing computational and storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Reinforcement Learning with Human Feedback (RLHF) is used to align model outputs to preferences, then the model can be fine-tuned to specific preferences, but the process becomes resource-intensive and complex requiring multiple training iterations and storage of base model parameters
Solution Approach 1:
The patent extracts and eliminates the complex reinforcement learning loop and separate reward model training from the fine-tuning process. Instead, it directly optimizes the language model using supervised fine-tuning on preference data, removing the unnecessary intermediate components while preserving the preference alignment capability
Solution Approach 2:
The patent merges the preference optimization objective directly into the language model training process. Rather than maintaining separate reward models and performing iterative reinforcement learning, the preference alignment is integrated into a single supervised fine-tuning process that directly modifies the language model parameters
2Adaptability or versatility
If controlled text generation methods are used to align model outputs, then the model can generate preferred text, but the generation process becomes expensive requiring additional inference steps for each token
Solution Approach 1:
The patent performs preference alignment during the training phase rather than during generation. By pre-training the model on preference data, the model learns to generate preferred text directly during standard generation without requiring additional inference steps or controlled text generation procedures at decode time
3Device complexity
If Direct Preference Optimization (DPO) is used for fine-tuning, then the process is simpler than RLHF, but the method is restricted to specific pairwise ranking loss limiting flexibility
Solution Approach 1:
The patent creates a universal fine-tuning framework that can handle multiple types of preference labels (pairwise, listwise, pointwise) and different loss functions through a single supervised fine-tuning process. The method uses a general objective function that can accommodate various preference formulations without requiring task-specific modifications
Data Source
AI summary
Provided is a methodology for direct supervised preference fine-tuning of sequence processing models such as, for example, so-called large language models (LLMs) and large multimodal models (LMMs). The proposed approaches can fine-tune the model to directly predict the posterior token probabilities conditioned on a positive preference of the sequence for which the token is the last token on a sequence of tokens that are the prefix to the sequence. This method offers a simpler fine-tuning approach that directly generates the desired posteriors for use in decoding, without requiring additional inference per vocabulary token at decoding time.