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arxiv: 2605.22769 · v2 · pith:JYY7QQ5Jnew · submitted 2026-05-21 · 💻 cs.CL · cs.AI

Understanding Data Temporality Impact on Large Language Models Pre-training

Hippolyte Pilchen , Romain Fabre , Franck Signe Talla , Patrick Perez , Edouard Grave This is my paper

Pith reviewed 2026-05-22 05:33 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords large language modelspre-training data ordertemporal knowledgefactual freshnesscontinual learningCommon Crawl snapshotstime-sensitive benchmarksshuffled vs sequential training
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The pith

Training LLMs on time-ordered data snapshots yields fresher and more precisely dated factual knowledge than the usual shuffled approach.

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

The paper examines how the sequence of training data affects what large language models learn about facts that change over time. Instead of mixing all data together as is standard, the authors train models on Common Crawl snapshots kept in chronological order. They built a benchmark of more than 7,000 questions that ask not only what is true but when it became true. Sequentially trained models perform as well as shuffled ones on general language tasks and timeless knowledge yet show clearer advantages on questions about recent events and on correctly linking facts to their proper periods. This matters because real knowledge evolves and current training practices leave models with knowledge that is frozen at the moment the data was mixed.

Core claim

Sequentially trained 6B-parameter models on temporally ordered Common Crawl snapshots match shuffled baselines on general language understanding and common knowledge while consistently exhibiting more up-to-date and temporally precise knowledge; temporally ordered pre-training improves factual freshness, whereas shuffled pre-training peaks on older data possibly because of greater factual repetition.

What carries the argument

The direct comparison of pre-training on temporally ordered Common Crawl snapshots versus standard shuffled corpora, measured through a new benchmark of over 7,000 temporally grounded questions that test whether models associate each fact with its correct time period.

If this is right

  • Ordered data during pre-training can deliver improved factual freshness without any loss on standard language understanding benchmarks.
  • Shuffled training tends to strengthen performance on older facts, possibly through repeated exposure to the same information.
  • Temporally ordered pre-training offers a practical route toward continual learning that keeps model knowledge current.
  • Releasing the ordered snapshots, checkpoints, and benchmark enables direct testing of temporal ordering in future model training runs.

Where Pith is reading between the lines

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

  • The same ordering principle might reduce staleness for other kinds of changing information such as scientific claims or cultural references.
  • Training pipelines could adopt chronological batching as a default low-cost change to limit knowledge decay after release.
  • The benchmark could serve as a diagnostic for other continual-learning methods that add new data after initial training.
  • Effects may grow or shrink at larger model scales, which would be testable by repeating the ordered-versus-shuffled comparison on bigger architectures.

Load-bearing premise

The benchmark of over 7,000 temporally grounded questions together with the evaluation protocol correctly isolates whether a model links each fact to its proper time period rather than measuring other correlated abilities or dataset artifacts.

What would settle it

A result in which sequentially trained models score no higher than shuffled models on questions about recent events, or score lower on questions about older events, would undermine the central claim.

Figures

Figures reproduced from arXiv: 2605.22769 by Edouard Grave, Franck Signe Talla, Hippolyte Pilchen, Patrick Perez, Romain Fabre.

Figure 1
Figure 1. Figure 1: Yearly temporal knowledge with Kairos. Relative gains in F1 score on KairosQA between the 2020–2021 and 2023–2024 periods for our sequentially pre-trained model versus other open-source base models (ordered by their release date with the most recent at the right). These results highlight that even for recently released open-source base models, shuffled pre-training leads to a temporal delay in knowledge; p… view at source ↗
Figure 2
Figure 2. Figure 2: Creation of KairosQA. Summary of the methodology for creating the dataset of temporally sensitive facts (top) and the evaluation protocol (bottom) for the proposed benchmark of time-grounded knowledge [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Learning Dynamics on OLMES. Evolution of general language understanding scores over 2.5T tokens. The Shuffle baseline exhibits higher efficiency in the mid-training phase, likely due to the stationary distribution of the data. The Sequential model steadily closes this gap, showing that chronological ordering alters the learning trajectory without compromising final model capacity. However, the learning tra… view at source ↗
Figure 4
Figure 4. Figure 4: Temporal evaluation on KairosQA. Comparison of sequential checkpoints versus the last shuffled checkpoint, since shuffled checkpoints exhibit nearly identical performance dynamics across all pre-training lengths (see in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison with open-source models. Temporal performance on KairosQA comparing our approach to various baseline models. (a) Displays accuracy in the cloze formulation, while (b) shows the corresponding F1 scores across evaluation years. To further validate these findings, we demonstrate that most recently released open-source models exhibit temporal knowledge dynamics aligned with our shuffled baseline As … view at source ↗
Figure 6
Figure 6. Figure 6: Cloze formulation robustness. We study the robustness of our protocol on our last checkpoint of the sequential pre-training by evaluating it on KairosQA for year 2024 in cloze formulation with an increasing number of choices. Cloze formulation robustness. Cloze-style evaluation can be criticized as overly simplistic because it relies on ranking a finite set of candidates rather than on open-ended generatio… view at source ↗
Figure 7
Figure 7. Figure 7: Effect of the popularity of the subject in the questions on our last checkpoint of the sequential pre-training by evaluating it on KairosQA for year 2024. Subject popularity. We also analyze question difficulty by categorizing subjects into popularity bins based on the proxy metric described in Section 3, which reflects subject frequency within the pre-training corpus. By targeting the most recent year (20… view at source ↗
Figure 8
Figure 8. Figure 8: Learning Dynamics on MMLU. Evolution of MMLU scores over 2.5T tokens. The Shuffle baseline exhibits higher efficiency in the mid-training phase, likely due to the stationary distribution of the data. The Sequential model steadily closes this gap, showing that chronological ordering alters the learning trajectory without compromising final model capacity. The two discontinuities at 200B tokens for the Shuff… view at source ↗
Figure 9
Figure 9. Figure 9: Temporal evaluation on KairosQA. Comparison of sequential checkpoints versus their shuffled counterparts in terms of total token count. Shuffled checkpoints exhibit nearly identical performance dynamics across all pre-training lengths. Shuff eq 202* denotes the shuffled baseline trained on the same total token count as Seq 202*. (a) Cloze task accuracy across 4 choices; the random baseline varies slightly … view at source ↗
Figure 10
Figure 10. Figure 10 [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Temporal evaluation on KairosQA. Comparison of our last sequential checkpoint suffering from “relative forgetting” versus our version with replay of older knowledge as an attempt to mitigate forgetting.(a) Cloze task accuracy across 4 choices; the random baseline varies slightly in cases where fewer than four choices were available. (b) Generative task performance measured by F1 score. To address forgetti… view at source ↗
Figure 12
Figure 12. Figure 12: Evaluation of several open-source base models and our two pre-trained models on TAQA (Zhao et al., 2024) in the standard setting. 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Eval Year 0.025 0.050 0.075 0.100 0.125 0.150 0.175 F1 Score Model Llama-3.1-8B (12/2023) Qwen3-14B (rel. 04/2025) Qwen3-4B (rel. 04/2025) Qwen3-8B (rel. 04/2… view at source ↗
Figure 13
Figure 13. Figure 13: Evaluation of several open-source base models and our two pre-trained models on TAQA (Zhao et al., 2024) using the time-aware prompting strategy to target 2018. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Evaluation of several open-source base models and our two pre-trained models on TAQA (Zhao et al., 2024) using the time-aware prompting strategy to target 2021. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_14.png] view at source ↗
read the original abstract

Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal grounding remains poorly understood. In this work, we study the impact of pre-training dynamics on the acquisition of time-sensitive factual knowledge, focusing specifically on data ordering. Our main contributions are twofold. First, we introduce a comprehensive benchmark of over 7,000 temporally grounded questions and an evaluation protocol that enables analysis of whether models correctly associate facts with their corresponding time periods. Second, we pretrain 6B-parameter models on temporally ordered Common Crawl snapshots and compare them against standard shuffled pre-training. Our results show that sequentially trained models match shuffled baselines on general language understanding and common knowledge while consistently exhibiting more up-to-date and temporally precise knowledge. Temporally ordered pre-training yields improved factual freshness, while shuffled pre-training peaks on older data, possibly due to increased factual repetition. These findings, along with the release of our code at https://github.com/kyutai-labs/kairos , checkpoints, and datasets at https://huggingface.co/collections/kyutai/kairos provide a foundation for future research on continual learning for LLMs.

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 / 2 minor

Summary. The manuscript examines the impact of data ordering during LLM pre-training on the acquisition of time-sensitive factual knowledge. It introduces a benchmark consisting of over 7,000 temporally grounded questions together with an evaluation protocol designed to test whether models correctly associate facts with their corresponding time periods. The authors pre-train 6B-parameter models on temporally ordered Common Crawl snapshots and compare them to standard shuffled pre-training, reporting that sequential training matches shuffled baselines on general language understanding and common knowledge while producing more up-to-date and temporally precise knowledge.

Significance. If the benchmark and evaluation protocol are shown to isolate temporal association rather than recency or frequency artifacts, the results would provide a concrete empirical basis for preferring temporally ordered pre-training when factual freshness is desired. The public release of code, checkpoints, and datasets is a clear strength that supports reproducibility and future work on continual learning.

major comments (2)
  1. [Evaluation Protocol] The abstract and evaluation protocol description provide no details on statistical controls, exact data volumes per snapshot, or controls for confounds such as recency bias and snapshot frequency. This information is load-bearing for the central claim that sequential training produces more temporally precise knowledge rather than simply favoring the most recent data seen.
  2. [Results and Discussion] The observation that shuffled pre-training peaks on older data 'possibly due to increased factual repetition' is presented without accompanying measurements of repetition rates or controls that would distinguish repetition effects from ordering effects.
minor comments (2)
  1. [Benchmark Construction] Clarify the exact number of snapshots used, the temporal span covered, and how the 7,000-question benchmark was constructed (e.g., source of facts, temporal distractors, and scoring rubric).
  2. [Abstract] The abstract states that sequentially trained models 'match' shuffled baselines on general understanding; report the precise scores and confidence intervals to allow readers to assess the magnitude of any small differences.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our work examining data ordering effects in LLM pre-training. We address each major comment below and have revised the manuscript to incorporate additional details and analysis where needed.

read point-by-point responses

  1. Referee: [Evaluation Protocol] The abstract and evaluation protocol description provide no details on statistical controls, exact data volumes per snapshot, or controls for confounds such as recency bias and snapshot frequency. This information is load-bearing for the central claim that sequential training produces more temporally precise knowledge rather than simply favoring the most recent data seen.

    Authors: We agree that these details strengthen the central claim and should be explicit. In the revised manuscript we have expanded the evaluation protocol section with exact token and document counts per Common Crawl snapshot, bootstrap confidence intervals on all temporal-precision metrics, and two new controls: (1) a frequency-matched ablation that equalizes snapshot exposure while preserving order, and (2) a recency-masked evaluation that removes the most recent 20 % of facts from the test set. These additions demonstrate that the observed temporal precision gains are not reducible to recency or frequency artifacts alone. revision: yes

  2. Referee: [Results and Discussion] The observation that shuffled pre-training peaks on older data 'possibly due to increased factual repetition' is presented without accompanying measurements of repetition rates or controls that would distinguish repetition effects from ordering effects.

    Authors: We accept that the original discussion offered only a qualitative hypothesis. We have added a new subsection that quantifies repetition by computing the mean occurrence count of temporally anchored factual n-grams across the ordered and shuffled corpora. We further include a controlled re-training experiment on repetition-equalized subsets; the results show that the temporal-precision advantage of sequential ordering persists even after repetition rates are matched, thereby separating the two effects. revision: yes

Circularity Check

0 steps flagged

Empirical comparison with no derivation chain or self-referential reduction

full rationale

This is a controlled empirical study that introduces a benchmark of temporally grounded questions and compares two pre-training regimes (sequential on ordered snapshots vs. shuffled) on 6B models. No equations, first-principles derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. Results are obtained by direct experimental measurement against external baselines and the new benchmark; nothing reduces to its own inputs by construction. The central claim is therefore independent of any circular step.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that the new benchmark isolates temporal association and that the only systematic difference between the two training runs is data order rather than snapshot-specific artifacts.

axioms (1)
  • domain assumption Temporally ordered Common Crawl snapshots isolate the effect of data ordering on temporal knowledge acquisition without confounding differences in data quality or volume.
    Invoked when the authors compare the two pre-training regimes and attribute differences to ordering.

pith-pipeline@v0.9.0 · 5748 in / 1268 out tokens · 56856 ms · 2026-05-22T05:33:34.545122+00:00 · methodology

discussion (0)

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