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This research proposes a unified framework for addressing training bottlenecks in language models by aligning supervision strategies with specific computational limitations across circuit formation, content availability, and route selection. By validating these interventions on a 350M-parameter model, the study demonstrates that targeted adjustments to data teaching methods yield superior learning outcomes compared to simply re-ranking difficult examples. The findings offer a concrete pathway for optimizing long-context reasoning and fact retention without relying solely on scaling compute or data volume.
What Should Data Teach? Moving Bottlenecks Across Circuit, Store, and Use
arXiv:2609.32991v1 Announce Type: new Abstract: What should data teach a language model at a particular point in training? A circuit view reveals three distinct bottlenecks: forming a computation, making its required content available, and selecting among available routes. A shared diagnosis-to-data principle connects them: localize the missing operation, preserve its causal relation, vary shortcut-bearing context, and re-audit the residual. Formation-sensitive selection and prerequisite ordering accelerate a binding-matching-transport path; a brief early prefix from the same training multiset retains a validation advantage through 100B tokens. Availability counterfactuals then distinguish writing content from invoking available memory, while paired supervision and context-opportunity ranking improve matched route decisions and long-context answer likelihood. A continuous 350M-model experiment connects the three interventions on the same facts: early circuit training improves subsequent learning, and the complete sequence outperforms stage-replacement controls on facts withheld from Use teaching. Independent query surfaces and opposed-source decisions expose conditional arbitration as the remaining frontier. Together, these results show why a change in the limiting operation calls for a change in supervision, not merely a new ranking of difficult examples.
Title:What Should Data Teach? Moving Bottlenecks Across Circuit, Store, and Use
Abstract:What should data teach a language model at a particular point in training? A circuit view reveals three distinct bottlenecks: forming a computation, making its required content available, and selecting among available routes. A shared diagnosis-to-data principle connects them: localize the missing operation, preserve its causal relation, vary shortcut-bearing context, and re-audit the residual. Formation-sensitive selection and prerequisite ordering accelerate a binding-matching-transport path; a brief early prefix from the same training multiset retains a validation advantage through 100B tokens. Availability counterfactuals then distinguish writing content from invoking available memory, while paired supervision and context-opportunity ranking improve matched route decisions and long-context answer likelihood. A continuous 350M-model experiment connects the three interventions on the same facts: early circuit training improves subsequent learning, and the complete sequence outperforms stage-replacement controls on facts withheld from Use teaching. Independent query surfaces and opposed-source decisions expose conditional arbitration as the remaining frontier. Together, these results show why a change in the limiting operation calls for a change in supervision, not merely a new ranking of difficult examples.
Machine Learning (cs.LG)
arXiv:2609.32991 [cs.LG]
(or arXiv:2609.32991v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.32991 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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