Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Research Methodology & Innovation · Depth: Expert, quick

Summary

A position paper submitted on June 3, 2026, and accepted as an oral presentation at ICML 2026, argues for a fundamental shift in AI research towards understanding training dynamics. Titled "Position: Don't Just \"Fix it in Post\": A Science of AI Must Study Training Dynamics," the paper contends that AI models are not static artifacts but rather time-evolving processes shaped by data, objectives, architectures, and optimization. It criticizes the prevalent approach of analyzing model behaviors post-training, which overlooks the causal mechanisms. The authors propose that a true science of AI must investigate how these dynamics produce specific model behaviors, aiming to predict outcomes from early training signals, intervene during problematic trajectories, and ultimately design training procedures that reliably yield desired properties like capabilities, robustness, and safety. While scaling laws have enabled loss prediction, extending this understanding to complex behaviors, biases, and safety remains a significant challenge, requiring new theories grounded in scientific philosophy.

Key takeaway

For AI Scientists and Research Scientists designing or evaluating complex models, you should prioritize investigating training dynamics rather than solely relying on post-hoc analysis. Understanding how data, objectives, and optimization shape model evolution is critical for predicting emergent behaviors, intervening effectively when issues arise, and reliably engineering desired properties like robustness and safety. This shift will enable you to move beyond reactive fixes towards proactive, principled AI development.

Key insights

A scientific understanding of AI requires studying training dynamics, not just post-training analysis, to predict and control model behaviors.

Principles

• AI models are dynamic, time-evolving processes.
• Training dynamics dictate model capabilities and safety.
• Scientific understanding requires studying behavior emergence.

Topics

• AI Training Dynamics
• Model Behavior Emergence
• Mechanistic Interpretability
• AI Safety
• Scaling Laws
• ICML 2026

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.