DARE
Dropping residual parameters to efficiently prune large language models without retraining.
DARE is a parameter-pruning method that drops the residual connections in a large language model's layers before merging them together, resulting in a compact model that retains most of the original capabilities without additional training. Unlike methods that require gradual fine-tuning, DARE achieves its compression in a single step by exploiting the mathematical structure of how transformer layers compose information.
The approach works by zeroing out the bias terms added by residual connections and then rescaling the remaining weights to preserve the model's effective behavior. This allows a 7-billion-parameter model to be reduced to roughly 5 billion parameters with minimal performance loss on standard benchmarks.
The key insight is that residual connections act as identity mappings, so their contributions can be eliminated without drastically altering what the network has learned. This mathematical property means that most of what a model knows lives in the feed-forward layers, not the skip connections.
DARE is particularly valuable for deployment scenarios where inference cost matters more than peak benchmark performance. A 30% reduction in parameters with single-digit capability loss can translate to meaningful latency and memory improvements on commodity hardware.
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