In compute-optimal regimes, language model parameter count scales proportionally with data bytes rather than tokens, and the optimal compression rate decreases with increasing compute.
Resolving Discrepancies in Compute-Optimal Scaling of Language Models , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.
citing papers explorer
-
Compute Optimal Tokenization
In compute-optimal regimes, language model parameter count scales proportionally with data bytes rather than tokens, and the optimal compression rate decreases with increasing compute.
-
Position: Agentic AI System Is a Foreseeable Pathway to AGI
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.