LLM pretraining scaling has hit a recent rough patch.
Assessment
The evidence favors the claim, but the examination is incomplete or the evidence is indirect.
The claim refers to the period from late 2024 through mid-2025, when the returns from simply scaling up LLM pretraining appeared to disappoint. Read that way, the evidence favors it. In November 2024, outlets reported that OpenAI's next-generation model ("Orion") and comparable efforts at other labs were showing smaller gains than past generational jumps. In December 2024, Ilya Sutskever said at NeurIPS that "pre-training as we know it will end" because high-quality data is finite. GPT-4.5, released in February 2025 as OpenAI's largest pretraining run, was widely judged disappointing on benchmarks given its cost. These developments support the premise that 2024–2025 scale-ups produced smaller gains than earlier transitions, and the approaching exhaustion of high-quality human text has been offered as a mechanism for the shortfall.
There are two caveats. First, a "rough patch" in returns measured on capabilities and benchmarks does not mean the scaling laws broke. Loss continued to improve predictably with compute, and part of the slowdown may reflect delays in building out compute and a shift of effort toward reinforcement-learning and inference-time scaling, rather than a fundamental limit. Second, the patch may have been temporary. Google attributed much of Gemini 3's large late-2025 gains to improved pretraining, and commentators took this as evidence that pretraining scaling was intact. As of late 2026, the claim holds best as a description of 2024–2025. Whether it is "recent" depends on the reader's vantage point. It is weaker if read as saying that pretraining scaling has stalled durably.
Full reasoning: the evidence and decisions behind this verdict
Prior model: the claim is true if frontier pretraining scale-ups in roughly 2024–25 underdelivered relative to cost and to previous generational jumps. It is false if returns continued smoothly. Evidence for: November 2024 reporting on Orion and similar efforts (e.g. www.deeplearning.ai/the-batch/ai-giants-rethink-model-training-strategy-as-scaling-laws-break-down, www.obsolete.pub/p/is-deep-learning-actually-hitting). Sutskever's December 2024 NeurIPS remark on peak data (analyticsindiamag.com/ai-features/the-end-of-pre-training-era-begins/). The reception of GPT-4.5 as disappointing (Zvi Mowshowitz; Nathan Lambert, www.interconnects.ai/p/gpt-45-not-a-frontier-model). A late-2025 retrospective describing labs as no longer seeing sufficient returns from scaling pretraining (www.transformernews.ai/p/where-have-the-really-big-ai-models-scaling-pretraining-inference-gpt-compute). Evidence against, or limiting the claim: Google DeepMind staff said Gemini 3 delivered a "drastic jump" from pretraining (tldr.tech/ai/2025-11-21, tomtunguz.com/gemini-3-proves-pretraining-scaling-laws-intact/). Noam Brown and others denied that AI was hitting a wall. The single recorded instance is Gary Marcus's April 2025 critique of AI 2027, which affirms the claim. Its date fits the patch. Weighing: the claim is vague ("rough patch", "recent"). That vagueness lets it absorb the strongest objection, that scaling laws in loss held, because a rough patch in practical returns is compatible with intact loss curves. The Gemini 3 evidence cuts against reading the claim as a durable stall more than against the 2024–25 episode. Attribution of gains between pretraining and post-training is hard to verify from outside, which limits both arguments. What would change this: lab disclosures of capability-per-compute for the 2024–2025 runs, or evidence that the slowdown was solely a compute-buildout delay.
Decomposition
How this claim breaks down: each argument is stated as it runs, with its subclaims linked inline. ↗︎ opens a subclaim; the map shows how they fit together.
Because frontier labs' 2024–2025 pretraining scale-ups produced smaller capability gains than earlier generational transitions, and because the stock of high-quality human text is nearly exhausted supplies a mechanism for that shortfall, pretraining scaling hit a rough patch in that period.
- Does it follow?
- likely if the premises holdIf the premises are true, the conclusion is much more likely, though not guaranteed.constitution →
- Do the premises hold?
- 1 supported1 not separately assessed
Granting that the 2024–2025 scale-ups produced smaller gains than earlier transitions, a "rough patch" follows almost directly, because the claim's loose wording asks little more than that. The argument therefore rests mainly on Frontier AI labs' 2024–2025 pretraining scale-ups produced smaller capability gains than earlier generational transitions.. Press reports and the reception of GPT-4.5 favor that premise, but it has not been independently assessed. The data-exhaustion premise adds a mechanism but is not needed for the conclusion.
Because Gemini 3 achieved large capability gains from continued pretraining scaling in late 2025, pretraining scaling was still delivering, so the reported slowdown was not a real setback for pretraining scaling as such but a lab-specific or temporary shortfall.
- Does it follow?
- slight even if the premises holdEven if the premises are true, they make the conclusion only a little more likely.constitution →
- Do the premises hold?
- 1 not separately assessed
Even granting that Gemini 3's gains came largely from pretraining, a later recovery does not show that there was no earlier setback. At most it shows the setback was temporary or specific to some labs. The argument bears more on whether the rough patch persists than on whether it occurred.
Provenance
Where this claim has been said, linked to its canonical form.
by now they know perfectly well that pretraining scaling has hit a rough patch.
Explaining why the scenario does not rely on pretraining scaling.
Cite this claim: a formal citation with its evidence attached
Contribute
Every judgment on this page is open to challenge. A contribution is evaluated on its merits by the reviewer; if it succeeds the page changes, and if it does not, the reasons are stated. Either way the exchange becomes part of the claim’s public record.
The attention this claim received was paid for by a funded mandate. Funding buys only scheduling: it can make an assessment happen sooner, or reach deeper into a subtree. It has no influence on what the assessment concludes, and none on which claims enter the graph; assessments run under the same public standards whoever pays, funders never see or shape a verdict before anyone else, and mandates that attempt to steer conclusions are refused.
Created by extractor · Nov 1, 2026. Every judgment on this page is accompanied by a reasoning trace.