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arxiv: 2606.00230 · v1 · pith:HSYI2ULGnew · submitted 2026-05-29 · 💻 cs.LG

A Pre-Training Analogue of Grokking in Language Models: Tracing Delayed Grammatical Generalization

Sherin Muckatira , Namrata Shivagunde , Vijeta Deshpande , Anna Rumshisky This is my paper

Pith reviewed 2026-06-28 22:57 UTC · model grok-4.3

classification 💻 cs.LG
keywords grokkinglanguage model pre-traininggrammatical generalizationBLiMPconcept vectorsattention headsdelayed generalizationminimal pairs
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The pith

Language models show delayed generalization on grammatical tasks long after relevant phrases first appear in pre-training data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets up an exposure-based split on BLiMP minimal pairs to track when language models begin to generalize on grammatical contrasts during ordinary pre-training. It reports that across five phenomena the models reach high accuracy on held-out examples only after a lag following the first appearance of the critical phrase. This lag is accompanied by measurable changes in how grammatical information is represented inside the model. Readers care because the result gives a concrete way to watch linguistic knowledge emerge in the actual pre-training regime rather than in artificial supervised loops.

Core claim

Across five grammatical phenomena, delayed generalization is observed on the proxy-validation split. After the generalization point, grammatical concept vectors become more predictive of acceptability judgments and occupy a higher-dimensional subspace. Attention from the critical token concentrates on the relevant context token in only a small number of heads.

What carries the argument

The exposure-based proxy-train and proxy-validation split, which assigns each BLiMP minimal pair according to whether its critical phrase has already appeared in the pre-training window.

If this is right

  • Grammatical generalization on unseen minimal pairs occurs only after sufficient exposure to the critical phrase.
  • Concept vectors extracted from model activations become stronger predictors of acceptability once generalization has taken place.
  • The linear subspace spanned by these concept vectors expands in dimension after the generalization threshold.
  • A small subset of attention heads carries most of the relevant token-to-context information at the generalization point.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same exposure-tracking method could be applied to other emergent capabilities to test whether delayed generalization is a general feature of pre-training.
  • If the proxy split is reliable, training schedules that deliberately increase the frequency of rare critical phrases might shorten the delay before generalization.
  • Changes in concept-vector dimensionality and predictiveness could serve as internal signals that generalization is about to occur on a given phenomenon.

Load-bearing premise

Whether a critical phrase has appeared in the pre-training window cleanly isolates the effect of data exposure from all other patterns that happen to co-occur in the corpus.

What would settle it

No correlation between the first appearance of a critical phrase in the pre-training data and the timing of generalization on the corresponding proxy-validation examples would falsify the exposure-driven account.

Figures

Figures reproduced from arXiv: 2606.00230 by Anna Rumshisky, Namrata Shivagunde, Sherin Muckatira, Vijeta Deshpande.

Figure 1
Figure 1. Figure 1: Delayed-generalization curves and cumulative C4 exposure for the analyzed BLiMP phenomena. Blue [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Concept-vector AUROC on the proxy-validation split across pre-training checkpoints. At each checkpoint, [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Effective rank of grammatical concept vectors across pre-training checkpoints for the five analyzed BLiMP [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average attention-to-context score across pre-training checkpoints. Each panel shows one BLiMP [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Delayed-generalization curves for the 35M model. Blue and red lines show accuracy on the proxy-train [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Delayed-generalization curves for the 130M model. Blue and red lines show accuracy on the proxy-train [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Concept-vector AUROC on the proxy-validation split across all layers. At each checkpoint and layer, [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Effective rank of grammatical concept vectors across all layers and pre-training checkpoints. Higher [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Mean attention entropy at the critical token across pre-training checkpoints. Each panel shows one BLiMP [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Token-level attention heatmaps for the heads with the largest increase in attention-to-context after delayed [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
read the original abstract

Grokking, the phenomenon in which neural networks generalize long after fitting their training data, has been studied in supervised settings on many epochs. LLM pre-training instead involves next-token prediction over an unlabeled corpus, with limited data repetition and no explicit train/validation split. To address this, we propose an exposure-based framework that enables the study of grokking-like dynamics during LLM pre-training. We ground our evaluation in BLiMP minimal pairs, which provide controlled grammatical contrasts. For every BLiMP minimal pair, we identify a critical phrase, the smallest continuous span that captures the grammatical contrast and the phenomenon-relevant context. Examples whose critical phrase appears in the pre-training window are assigned to the proxy-train split; the remaining examples are assigned to the proxy-validation split. Across five grammatical phenomena, we observe delayed generalization. Analyzing pre-training checkpoints before and after generalization shows that grammatical concept vectors become more predictive of grammatical acceptability and occupy a higher-dimensional subspace after generalization. We also find that attention from the critical token to the relevant context token is concentrated in a small number of heads.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes an exposure-based framework to study grokking-like delayed generalization during LLM pre-training. For BLiMP minimal pairs, it identifies a critical phrase capturing the grammatical contrast, assigns examples to a proxy-train split if the phrase appears in the pre-training window and to proxy-validation otherwise, and reports delayed generalization on the proxy-validation set across five grammatical phenomena. Checkpoint analysis shows grammatical concept vectors become more predictive of acceptability and occupy higher-dimensional subspaces after generalization, with attention from the critical token concentrated in a small number of heads.

Significance. If the proxy framework validly isolates exposure effects and the reported patterns hold with quantitative support, the work supplies a concrete method for tracing delayed generalization in large-scale unsupervised pre-training and links internal representation changes to grammatical acquisition, extending grokking studies beyond supervised toy settings.

major comments (2)
  1. [Abstract / Methods (proxy split)] Abstract / proxy-split definition: the assignment of examples to proxy-train versus proxy-validation rests solely on whether the critical phrase appears in the pre-training window. This definition does not include reported controls for lexical overlap, syntactic neighbors, or semantic similarity outside the phrase span; if such confounders drive incremental corpus statistics, the observed delay on proxy-validation cannot be attributed to a grokking-like mechanism triggered by exposure to the grammatical contrast.
  2. [Abstract / Results] Results (delayed generalization claims): the abstract states that delayed generalization is observed across five phenomena, yet supplies no quantitative metrics (e.g., accuracy curves, delay measured in tokens or steps, error bars, or statistical tests comparing pre- and post-generalization performance). Without these, the magnitude and reliability of the central empirical claim cannot be evaluated.
minor comments (1)
  1. [Abstract] Clarify the exact procedure for identifying the 'critical phrase' (smallest continuous span) for each BLiMP phenomenon so that the proxy splits are reproducible.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below, proposing revisions to strengthen the manuscript where the concerns identify gaps in the current presentation.

read point-by-point responses

  1. Referee: [Abstract / Methods (proxy split)] Abstract / proxy-split definition: the assignment of examples to proxy-train versus proxy-validation rests solely on whether the critical phrase appears in the pre-training window. This definition does not include reported controls for lexical overlap, syntactic neighbors, or semantic similarity outside the phrase span; if such confounders drive incremental corpus statistics, the observed delay on proxy-validation cannot be attributed to a grokking-like mechanism triggered by exposure to the grammatical contrast.

    Authors: The proxy split is intentionally defined around the critical phrase—the minimal span encoding the grammatical contrast—to isolate the effect of exposure to that contrast during pre-training. BLiMP minimal pairs already constrain lexical and semantic differences outside the contrast by design. Nevertheless, we acknowledge that explicit additional controls (e.g., lexical-overlap matching and embedding-based similarity filtering on non-critical tokens) would further rule out incremental corpus statistics as an alternative explanation. We will add these controls and corresponding analyses in the revised manuscript. revision: yes

  2. Referee: [Abstract / Results] Results (delayed generalization claims): the abstract states that delayed generalization is observed across five phenomena, yet supplies no quantitative metrics (e.g., accuracy curves, delay measured in tokens or steps, error bars, or statistical tests comparing pre- and post-generalization performance). Without these, the magnitude and reliability of the central empirical claim cannot be evaluated.

    Authors: The main text and supplementary figures contain the requested quantitative elements: accuracy curves with error bars, delay measured in tokens, and statistical comparisons of pre- versus post-generalization performance. To improve the abstract's informativeness while remaining within length limits, we will incorporate concise quantitative highlights (e.g., average delay across the five phenomena and post-generalization accuracy lift) in the revised abstract. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The proxy-train and proxy-validation assignment is defined solely from independent data exposure (critical phrase presence in the pre-training window), with no equations or self-citations reducing the reported delayed generalization, concept vector predictiveness, or subspace dimensionality to a fitted parameter or self-defined quantity from the same performance measure. All observations are empirical measurements on external BLiMP benchmarks across checkpoints; the methodology is self-contained against those benchmarks with no load-bearing self-citation chains or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central observations rest on the assumption that critical-phrase exposure defines a clean train/validation proxy and that vector subspace changes reflect genuine concept acquisition; no free parameters or invented physical entities are described.

axioms (1)
  • domain assumption BLiMP minimal pairs provide controlled grammatical contrasts that can be isolated via a single critical phrase span.
    Used to assign proxy splits and measure generalization.
invented entities (2)
  • critical phrase no independent evidence
    purpose: Smallest continuous span capturing the grammatical contrast for proxy split assignment.
    New construct introduced to operationalize exposure in pre-training data.
  • proxy-train split no independent evidence
    purpose: Examples whose critical phrase appears in the pre-training window.
    Invented split to approximate train/validation in unlabeled corpus.

pith-pipeline@v0.9.1-grok · 5736 in / 1308 out tokens · 17918 ms · 2026-06-28T22:57:54.381917+00:00 · methodology

discussion (0)

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Reference graph

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