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Forward Pass Domain Adaptation (Without Cross-Layer Backpropagation)
Not provided in the abstract
fine-tuningdomain adaptationtransformers
2608.14563
Builder Relevance
1h ago80%
Abstract
FPO adapts large language models efficiently without a backward pass, achieving significant throughput and memory savings while maintaining performance.
Reality Card
Core Claim
FPO achieves 2.7-3.2x throughput of standard fine-tuning with ~40% less peak training memory while maintaining performance on off-domain benchmarks.
Method / Result
FPO computes a single error signal at the output and applies it to each target layer, resulting in in-domain perplexity improvement.
Limitations
The method does not construct an autograd graph, which may limit its applicability to certain model architectures.
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