OpenNovelty.org

Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Large Language ModelsDownstream MetricsPretrainingEvaluationBenchmarksLLM
Abstract:

While scaling laws for Large Language Models (LLMs) traditionally focus on proxy metrics like pretraining loss, predicting downstream task performance has been considered unreliable. This paper challenges that view by proposing a direct framework to model the scaling of downstream accuracy from the training budget. We demonstrate that for a fixed token-to-parameter ratio, a simple two-parameter scaling law accurately describes this relationship. Our findings are validated by experiments on models with up to 17B parameters trained on up to 350B tokens, showing that downstream capabilities scaling can be described using a scaling law. Furthermore, we extend this framework to extrapolate and predict accuracy of target model with up to 6.7x larger training budget based on a set of smaller experiments. We will release a complete list of model losses and downstream evaluation results at various different scales to support reproducibility and encourage future research.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a two-parameter scaling law to predict downstream task accuracy directly from training budget, focusing on fixed token-to-parameter ratios. It sits within the 'Downstream Task Performance Prediction' leaf of the taxonomy, which contains three papers total. This leaf is part of the broader 'Scaling Law Formulation and Validation' branch, indicating a moderately populated research direction. The taxonomy shows five sibling leaves under this branch, suggesting the field has diversified into multiple prediction paradigms beyond classical loss-based scaling.

The taxonomy reveals neighboring work in 'Compute-Optimal Training Regimes' (three papers) and 'Over-Training and Extended Training Regimes' (two papers), both focused on loss prediction rather than downstream metrics. The 'Benchmark Performance Prediction' leaf (one paper) addresses aggregate scores across tasks, while 'Refined Scaling Law Formulations' (one paper) explores advanced mathematical models. The paper's emphasis on direct accuracy prediction distinguishes it from these loss-centric approaches, though it shares the broader goal of extrapolating performance from limited experiments with compute-optimal studies.

Among thirty candidates examined, the first contribution (two-parameter scaling law) shows two refutable candidates from ten examined, suggesting some prior overlap in mathematical formulations. The second contribution (prediction framework) and third contribution (cross-ratio extension) each examined ten candidates with zero refutations, indicating these aspects may be less directly addressed in the limited search scope. The statistics suggest the core scaling law formulation has more substantial prior work, while the framework's application to extrapolation and cross-ratio generalization appears less explored within the examined literature.

Based on the top-thirty semantic matches and taxonomy structure, the work appears to advance a moderately active research direction. The limited search scope means the analysis captures nearby prior art but cannot confirm exhaustive novelty. The taxonomy's hierarchical organization suggests the field is maturing, with distinct clusters for different prediction targets, though downstream accuracy prediction remains less densely populated than loss-based scaling studies.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: predicting downstream task performance from training budget in large language models. The field has organized itself around several complementary perspectives. Scaling Law Formulation and Validation focuses on deriving and testing mathematical relationships between compute, data, model size, and performance—ranging from foundational compute-optimal studies like Training Compute-Optimal[2] to specialized downstream prediction frameworks such as Predicting Downstream Performance[20] and Scaling Laws Downstream[46]. Resource Allocation and Optimization addresses how to distribute limited budgets across model configurations and training stages, while Specialized Training Paradigms explores alternative architectures and training recipes that may alter standard scaling behaviors. Performance Modeling and Prediction Infrastructure provides the tooling and benchmarks needed to validate these predictions, and Application-Driven Efficiency Studies examine domain-specific constraints. Surveys and Broad Overviews, including LLM Survey[3], synthesize these threads for practitioners seeking actionable guidance. A particularly active line of work centers on refining downstream task predictability beyond simple loss-based extrapolation. Scaling Downstream Metrics[0] directly tackles this challenge by developing methods to forecast task-specific performance from early training signals, positioning itself alongside Predicting Downstream Performance[20] and Scaling Laws Downstream[46] within the Downstream Task Performance Prediction cluster. These efforts contrast with broader compute-optimal frameworks like Training Compute-Optimal[2] and Compute-Optimal Training Analysis[7], which primarily optimize pre-training loss rather than end-task metrics. Meanwhile, works such as RL Post-Training Scaling[22] and Inference Compute Scaling[27] extend the prediction problem into post-training and inference regimes, highlighting open questions about how scaling laws transfer across different phases of the model lifecycle. Scaling Downstream Metrics[0] emphasizes early-stage forecasting for specific benchmarks, offering a more granular lens than the loss-centric approaches that dominate much of the scaling literature.

Claimed Contributions

Direct two-parameter scaling law for downstream accuracy

The authors introduce a direct scaling law that models downstream benchmark accuracy as a function of training FLOPs using only two parameters, eliminating the need for intermediate proxy metrics like pretraining loss. This approach is shown to be simpler and more accurate than existing two-stage methods.

10 retrieved papers
Can Refute
Framework for predicting downstream performance from pretraining budget

The authors develop a framework that directly maps pretraining compute budget to downstream task accuracy without relying on intermediate proxy metrics. They validate this framework across 130 experiments spanning models up to 17B parameters trained on 350B tokens.

10 retrieved papers
Extension of scaling law across token-to-parameter ratios and repeated sampling

The authors generalize their scaling law to handle different token-to-parameter ratios and derive a formula for modeling pass@k rates in code generation tasks as a function of both training compute and number of samples.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Direct two-parameter scaling law for downstream accuracy

The authors introduce a direct scaling law that models downstream benchmark accuracy as a function of training FLOPs using only two parameters, eliminating the need for intermediate proxy metrics like pretraining loss. This approach is shown to be simpler and more accurate than existing two-stage methods.

Contribution

Framework for predicting downstream performance from pretraining budget

The authors develop a framework that directly maps pretraining compute budget to downstream task accuracy without relying on intermediate proxy metrics. They validate this framework across 130 experiments spanning models up to 17B parameters trained on 350B tokens.

Contribution

Extension of scaling law across token-to-parameter ratios and repeated sampling

The authors generalize their scaling law to handle different token-to-parameter ratios and derive a formula for modeling pass@k rates in code generation tasks as a function of both training compute and number of samples.

Table of Contents