Loss Prediction Across Model Scales Via Empirical Scaling Law Interpolation

via “scaling-law-prediction-engine”

ultrascale-playbook — AI demo on HuggingFace

Unique: Encapsulates scaling law models in a web-accessible API layer via Gradio, making empirical scaling relationships available without requiring users to implement or tune their own models. Likely uses published research (Chinchilla, Kaplan et al.) as the foundation.

vs others: More convenient than manually implementing scaling law formulas or running empirical studies, while more flexible than fixed lookup tables because it supports continuous parameter variation.

via “scaling-law-extrapolation-analysis”

* ⭐ 06/2022: [Solving Quantitative Reasoning Problems with Language Models (Minerva)](https://arxiv.org/abs/2206.14858)

Unique: BIG-bench's scaling analysis is built on a diverse task set (204 tasks) rather than a single benchmark, allowing researchers to observe how different capability types scale differently — some tasks show smooth power-law scaling while others exhibit sudden emergence or saturation, providing richer insights than single-benchmark scaling studies

vs others: More comprehensive than single-task scaling studies (e.g., MMLU alone) because it reveals that scaling laws vary dramatically by task type, preventing overgeneralization from narrow benchmarks

* ⭐ 04/2022: [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (SayCan)](https://arxiv.org/abs/2204.01691)

Unique: Fits bidirectional power-law scaling laws (loss as function of both parameters AND tokens) rather than unidirectional extrapolation; discovers that optimal loss follows a specific compute-dependent curve where both parameter and token exponents are nearly identical (~-0.07), enabling unified compute-optimal recommendations.

vs others: More accurate than prior Kaplan scaling laws for predicting loss at new scales because it accounts for both parameter and token scaling simultaneously; enables loss prediction without retraining, saving weeks of compute compared to empirical validation