Label-free invariances outperform supervised priors in grokking acceleration
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
Researchers have identified the precise conditions under which representational priors accelerate neural network generalization during grokking, a phenomenon where models suddenly shift from memorization to learning. The work isolates three critical factors: priors must align with the circuit's actual feature structure rather than arbitrary partitions, label-free invariances can outperform supervised constraints, and weight regularization amplifies these effects. These findings directly inform how practitioners design inductive biases to compress training timelines and improve sample efficiency, with implications for both interpretability and practical model development.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
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The more counterintuitive finding buried in this work is that label-free invariances, constraints derived from the data's structure rather than its class labels, can outperform supervised constraints when shaping what a network learns to ignore. That flips a common assumption that more task-specific guidance is always better.
This connects directly to the same-day work on CoCo contrastive loss (index 1), which also targets how networks form compact, well-separated representations during training. Both papers are essentially asking the same upstream question from different angles: what signals, built into the training process before a single label is consumed, determine whether a model learns something general or something brittle. Where CoCo focuses on the loss geometry, this grokking paper focuses on the prior structure, and together they suggest practitioners have more levers to pull on representation quality than the standard 'get more data' answer implies. The weight regularization finding here also echoes the physics-informed work on tensegrity structures (index 4), where embedding domain constraints directly into training consistently outperformed post-hoc correction.
If these conditions generalize beyond the modular arithmetic tasks typically used in grokking research and hold on standard vision or language benchmarks, the label-free invariance result becomes a practical design principle. Watch for follow-up work testing these priors on tasks where ground-truth circuit structure is less obvious.
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·arXiv cs.LG
New loss function accelerates neural network convergence on tabular data
Researchers propose CoCo, a contrastive loss function designed to improve how neural networks learn compact, well-separated class representations. The method balances intra-class clustering with inter-class distinction while maintaining geometric flexibility, addressing a core challenge in representation learning. Theoretical analysis shows CoCo converges faster and produces more informative gradients than cross-entropy and related objectives. Validation on…
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “What Makes a Representational Prior Work? Feature Families, Label-Free Invariances, and Critical Windows in Grokking”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.