Forecasting Downstream Performance of LLMs With Proxy Metrics

2026-05-18 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new method proposes using proxy metrics derived from token-level statistics to forecast the downstream performance of large language models (LLMs). This approach addresses limitations of traditional cross-entropy loss, which poorly aligns with downstream capabilities, and expensive direct downstream evaluations. The proxy metrics aggregate data like entropy, top-k accuracy, and expert token rank from a candidate model's next token distribution over expert-written solutions. This method consistently outperforms loss- and compute-based baselines across three scenarios: ranking heterogeneous reasoning models with a mean Spearman Rho of 0.81 (compared to 0.36 for cross-entropy loss), reliably ranking 25 candidate pretraining corpora at 10,000x less compute, and extrapolating downstream accuracy over an 18x compute horizon with approximately half the error of existing alternatives. These findings indicate that expert trajectories offer a valuable signal for assessing model capabilities throughout the development lifecycle.

Key takeaway

For NLP Engineers and Research Scientists making critical architecture or pretraining corpus decisions, integrating these new proxy metrics can significantly improve performance forecasting. Your team can achieve more reliable model selection and data curation with substantially less computational expense, potentially accelerating development cycles and optimizing resource allocation. Consider implementing these expert trajectory-based proxies to enhance early-stage model evaluation and reduce costly direct downstream testing.

Key insights

Proxy metrics from expert trajectories reliably forecast LLM downstream performance, outperforming traditional loss and compute baselines.

Principles

• Cross-entropy loss poorly aligns with downstream LLM capabilities.
• Expert trajectories provide strong signals for model assessment.

Method

Construct proxy metrics by aggregating token-level statistics (entropy, top-k accuracy, expert token rank) from a model's next token distribution over expert-written solutions.

In practice

• Use proxy metrics for cross-family model selection.
• Apply proxies for efficient pretraining data selection.
• Forecast training-time accuracy with reduced error.

Topics

• LLM Performance Forecasting
• Proxy Metrics
• Token-level Statistics
• Expert Trajectories
• Model Selection

Best for: Research Scientist, NLP Engineer, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.