Training data structure drives compositional reasoning failures in transformers
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers have identified why transformer models fail to generalize compositional reasoning beyond their training distribution, even when they master the same logical steps in-sample. By studying GPT-2-scale models in controlled two-hop reasoning tasks, the work traces compositional breakdown to how atomic facts are structurally presented during training. The finding suggests that OOD transfer failures stem not from fundamental architectural limits but from misalignment between training data layout and the circuits models use to chain reasoning steps. This has direct implications for scaling reasoning capabilities and designing training regimes that produce more robust, generalizable language models.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
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The paper isolates the culprit: compositional failure isn't due to transformers lacking the capacity to reason, but rather stems from how training data layout misaligns with the internal circuits models build to chain reasoning steps. This shifts the problem from architectural to empirical.
This connects directly to two recent findings. The Structure Gap paper (late October) formalized how raw scale cannot overcome structural misalignment between task requirements and learner architecture. Here, we see a concrete instance: the structure of how facts are presented during training creates a mismatch with how the model's circuits naturally compose reasoning. Separately, the mechanistic study on RL-trained LLMs (October 29) found models exploit surface patterns rather than build robust reasoning. This work suggests the inverse problem: even when models master logical steps in-distribution, the structural presentation of those steps determines whether they generalize, implying that reasoning robustness depends on training regime design, not just post-training optimization.
If researchers can show that reordering or restructuring how atomic facts appear in training data (without changing the facts themselves) recovers OOD compositional generalization on the same two-hop tasks, that confirms the hypothesis. If generalization remains broken despite structural tweaks, the problem runs deeper than circuit alignment.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.LG
Researchers formalize the limits of scale without structural knowledge
A new arXiv paper challenges the scaling hypothesis by identifying a fundamental gap between neural learners trained on raw data and systems that receive explicit structural knowledge. The work formalizes how tasks governed by known constraints, symmetries, physical laws, or combinatorial structure create a finite-sample identification problem that scale alone cannot solve. This directly impacts…
MentionsGPT-2 · Transformers
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Implicit Reasoning in Transformers: Bridging the Compositionality Gap in GPT-2-like Models”. 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.