Post by sabbirislam258 on Feb 14, 2024 6:30:31 GMT
The key insight is that the weighted average retains only the invariant information that is learned across the different RMs. This reduces reliance on spurious signals, increasing robustness. Its coupling also benefits from a reduction in variability, improving reliability despite distribution changes. As discussed earlier, diversity in independently trained models is critical to unlocking the full potential of model integration. The WARM paper explores a few clever ideas that could generalize more widely: Ordering Shuffles A simple but effective approach is changing the order in which the data points are viewed by each model during training.
Even this simple step eliminates weight, reducing redundant Russia Telemarketing Data memory of the pattern. Hyperparameter variations Tuning hyperparameters such as learning rate and dropout probability for each run introduces useful diversity. Models converge differently, capturing distinct features of the dataset. Checkpoint Averaging - Baklava The Baklava method initializes models with the same prior training trajectory to merge from different snapshots. This model reduces barriers compared to soup which mandates a common starting point. A relative of the model ratatouille, baklava avoids extra work. Overall, it strikes an effective balance of accuracy and diversity. Fixing multiple reward models The process starts with a pre-trained Large Language Model (LLM) 𝜃_𝑝𝑡.
From this model, different checkpoints are derived during a supervised fine-tuning (SFT) run, each of which is collected at different SFT training steps. These checkpoints are then used initially to fine-tune multiple reward models (RMs) {𝜙𝑖} on the preferred dataset. The purpose of this fine-tuning is to better align the models with human preferences. After fine-tuning, these RMs are combined through a weighted averaging process, resulting in the final model, 𝜙_WARM. The analysis confirms that the addition of old outliers to moving averages harms individual performance, compromising diversification benefits.
Even this simple step eliminates weight, reducing redundant Russia Telemarketing Data memory of the pattern. Hyperparameter variations Tuning hyperparameters such as learning rate and dropout probability for each run introduces useful diversity. Models converge differently, capturing distinct features of the dataset. Checkpoint Averaging - Baklava The Baklava method initializes models with the same prior training trajectory to merge from different snapshots. This model reduces barriers compared to soup which mandates a common starting point. A relative of the model ratatouille, baklava avoids extra work. Overall, it strikes an effective balance of accuracy and diversity. Fixing multiple reward models The process starts with a pre-trained Large Language Model (LLM) 𝜃_𝑝𝑡.
From this model, different checkpoints are derived during a supervised fine-tuning (SFT) run, each of which is collected at different SFT training steps. These checkpoints are then used initially to fine-tune multiple reward models (RMs) {𝜙𝑖} on the preferred dataset. The purpose of this fine-tuning is to better align the models with human preferences. After fine-tuning, these RMs are combined through a weighted averaging process, resulting in the final model, 𝜙_WARM. The analysis confirms that the addition of old outliers to moving averages harms individual performance, compromising diversification benefits.