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scaling-laws

topic4 events
papersTODAY 04:00 UTC

Study Ties Data and Memory Scaling to a Single Predictive Spectrum

A new arXiv paper argues that the benefit a model gains from more data and the amount of learned memory it needs are both determined by one predictive-energy spectrum in a positive-entropy autoregressive retrieval source. In this framework, each coordinate's contribution is the product of its query probability and a per-coordinate term, linking the two resources analytically. The work offers a theoretical account of how data volume and memory capacity trade off in autoregressive prediction.

research/scaling/memory/autoregressive
papersTODAY 04:00 UTC

Paper Argues LLMs Act as Lossy Compressors, Not Solomonoff Induction Estimators

A new arXiv paper examines the widely discussed question of whether large language models function as Solomonoff induction estimators, a topic bridging algorithmic information theory and machine learning. The authors contend that LLMs instead behave as Shannon-style lossy compressors, and they argue that major capability leaps would require symbolic model synthesis carried out in program space rather than scaling alone.

arxiv/theory/compression/llm-capabilities
papersTODAY 04:00 UTC

arXiv Paper Analyzes Optimal Learning Rate Schedules Under Functional Scaling Laws

A new arXiv preprint examines how learning rate schedules can be optimized within the functional scaling law framework, which separates training dynamics into signal learning and noise forgetting. The authors analyze power-law kernel regression to characterize these two components, comparing schedules such as power decay and warmup-stable-decay. The work offers theoretical guidance on choosing learning rate schedules for model training.

training/optimization/scaling-laws/learning-rate
papersSEP 12 04:00 UTC

Study Maps Scaling Laws Behind Grokking's Delayed Generalization

A new arXiv preprint examines grokking, the phenomenon where neural networks keep memorizing training data before abruptly improving on held-out data. While prior work has focused on why this delay happens, the paper targets its quantitative structure, describing scaling laws and a phase structure that predict when the shift occurs. The authors present an arXiv preprint; the abstract excerpt provided does not detail the full experimental setup or results.

grokking/scaling-laws/research/training-dynamics