Scaling Laws For Data-Constrained Dense Retrieval
Dense retrieval models are typically obtained by fine-tuning pretrained language models on supervised retrieval data, where high-quality annotations or interaction logs are often limited and reused across many epochs. This data-constrained regime differs from the abundant-data setting assumed by standard scaling laws, making it unclear how the best attainable retrieval performance changes with model size and unique fine-tuning data. We study this question through the minimum achievable evaluation loss (MAEL), defined as the lowest evaluation loss reached during training under fixed model-size and data-budget constraints. We first show that learning-rate choices strongly affect optimization trajectories but have little effect on the final attainable MAEL: after rescaling the optimization-step axis, learning curves collapse across learning rates, and CKA analysis shows that checkpoints reaching MAEL learn highly similar representations. We then fine-tune 35 BERT-family dense retrievers on MS MARCO subsets and find that MAEL is accurately described by a simple multiplicative power law over non-embedding parameters and unique samples, outperforming commonly used additive scaling formulations. We further derive a scaling law for the training step at which MAEL is reached and show that optimal weight decay follows an approximately invariant EMA timescale, yielding lower MAEL and stronger retrieval performance on both in-domain and out-of-domain benchmarks. Finally, combining loss and step scaling suggests that, in our observed regime, increasing pretrained model size is approximately 1.46 times as fine-tuning-compute-efficient as increasing unique in-domain data for reducing MAEL.