LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws
Researchers propose a Shannon-theoretic framework for LLM scaling that reinterprets model training as noisy-channel communication, offering a unified explanation for non-monotonic performance phenomena like catastrophic overtraining and quantization collapse. This perspective maps model parameters to channel bandwidth and training tokens to signal power, suggesting a fundamental capacity ceiling where scaling without maintaining signal-to-noise ratio yields diminishing or negative returns. The work challenges conventional power-law scaling assumptions and could reshape how practitioners think about compute allocation, data quality, and model size trade-offs in production systems.
Modelwire context
ExplainerThe most underplayed element in the summary is the specific claim about catastrophic overtraining: this framework offers a mechanistic explanation, not just an empirical observation, for why continuing to train past a certain point actively degrades performance rather than simply plateauing. That distinction matters because it shifts the conversation from 'how much data is enough' to 'what is the noise floor of your training corpus.'
This is largely disconnected from recent activity in our archive, as we have no prior coverage to anchor it to. It belongs to a slow-building theoretical current in ML research that has been quietly challenging the dominance of Chinchilla-style power-law scaling intuitions. That debate has practical stakes: teams at major labs have been making multi-hundred-million-dollar compute allocation decisions based on scaling laws that this paper argues are incomplete descriptions of an underlying information-theoretic reality.
The framework's credibility hinges on whether its predicted capacity ceilings hold empirically across model families beyond the cases cited in the paper. If an independent group replicates the quantization collapse predictions on a publicly released model series within the next six months, the theoretical scaffolding becomes much harder to dismiss.
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsShannon-Hartley theorem · Large Language Models · Shannon Scaling Law
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