Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families

TL;DR

This paper introduces Skills Scaling Laws (SSLaws), a novel approach that predicts large language model performance across multiple benchmarks by modeling low-dimensional latent skills influenced by computational resources.

Contribution

SSLaws leverages benchmark correlations and latent skill modeling to improve performance predictions across LLM families without extensive retraining.

Findings

Accurately predicts LLM performance on 12 benchmarks.

Provides insights into skill scaling and compute efficiency.

Reduces need for training multiple models per family.

Abstract

Scaling laws for large language models (LLMs) predict model performance based on parameters like size and training data. However, differences in training configurations and data processing across model families lead to significant variations in benchmark performance, making it difficult for a single scaling law to generalize across all LLMs. On the other hand, training family-specific scaling laws requires training models of varying sizes for every family. In this work, we propose Skills Scaling Laws (SSLaws, pronounced as Sloth), a novel scaling law that leverages publicly available benchmark data and assumes LLM performance is driven by low-dimensional latent skills, such as reasoning and instruction following. These latent skills are influenced by computational resources like model size and training tokens, but with varying efficiencies across model families. Sloth exploits correlations across benchmarks to provide more accurate and interpretable predictions while alleviating the need to train multiple LLMs per family. We present both theoretical results on parameter identification and empirical evaluations on 12 prominent benchmarks, from Open LLM Leaderboard v1/v2, demonstrating that Sloth predicts LLM performance accurately and offers insights into scaling behaviors for complex downstream tasks, increased test-time compute, and compute-optimal scaling of skills.