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[2502.17356] Random Scaling of Emergent Capabilities
Summary
This article explores the phenomenon of emergent capabilities in language models, proposing that performance breakthroughs are influenced by random seed variations rather than solely by scaling laws.
Why It Matters
Understanding the mechanics behind emergent capabilities in AI models is crucial for researchers and developers. This insight can inform better model training strategies and expectations regarding performance improvements, ultimately impacting AI development and application in various fields.
Key Takeaways
- Emergent capabilities in language models may be driven by random seed variations.
- Performance breakthroughs can occur at capacity thresholds before most seeds achieve them.
- Bimodal distributions of training outcomes can explain sudden performance improvements.
- Continuous changes in probability distributions are critical for understanding model performance.
- Random variation must be considered when predicting model performance from scale.
Computer Science > Machine Learning arXiv:2502.17356 (cs) [Submitted on 24 Feb 2025 (v1), last revised 18 Feb 2026 (this version, v5)] Title:Random Scaling of Emergent Capabilities Authors:Rosie Zhao, Tian Qin, David Alvarez-Melis, Sham Kakade, Naomi Saphra View a PDF of the paper titled Random Scaling of Emergent Capabilities, by Rosie Zhao and 4 other authors View PDF HTML (experimental) Abstract:Language models famously improve under a smooth scaling law, but some specific capabilities exhibit sudden breakthroughs in performance. Advocates of "emergence" view these capabilities as unlocked at a specific scale, but others attribute breakthroughs to superficial metric thresholding effects. We propose that breakthroughs are instead driven by continuous changes in the probability distribution of training outcomes when performance is bimodally distributed across random seeds. we show that different random seeds can produce either smooth or emergent scaling trends in synthetic length generalization tasks, multiple choice question answering, and grammatical generalization. We reveal that sharp breakthroughs in metrics are produced by underlying continuous changes in their distribution across seeds. These distributions may become abruptly bimodal at a capacity threshold but this threshold appears at scales well before most seeds achieve breakthrough. Our observations hold true even under continuous loss metrics, confirming that random variation must be considered when predictin...
