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Scaling New Frontiers: Insights into Large Recommendation Models

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that HSTU, a transformer-based generative recommendation model, scales with depth in recall and ranking while GPT and SASRec do not, and that residual connections and relative attention bias are the source of the scaling…

desk verdict Useful but uneven empirical study: the HSTU ranking evaluation is new and worth having, but the central scaling-law contrast is under-evidenced. read the letter →

arxiv 2412.00714 v1 pith:SXLQVYXJ submitted 2024-12-01 cs.IR

Wei Guo , Hao Wang , Luankang Zhang , Jin Yao Chin , Zhongzhou Liu , Kai Cheng , Qiushi Pan , Yi Quan Lee
show 11 more authors
Wanqi Xue Tingjia Shen Kenan Song Kefan Wang Wenjia Xie Yuyang Ye Huifeng Guo Yong Liu Defu Lian Ruiming Tang Enhong Chen
This is my paper · ORCID
classification cs.IR
keywords scalinglawlargerecommendationmodelsgenerativeHSTUsequentialrankingtaskmulti-behaviormodelingmulti-domain
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that scaling laws for large recommendation models exist but depend on architecture: as the number of transformer blocks grows, HSTU and Llama keep improving on recall, while GPT and SASRec stall or degrade. It further claims that HSTU, the representative generative recommendation backbone, also scales in ranking tasks, and that the origin of this scaling lies in the residual-connection pattern plus a relative attention bias built on position and time-difference buckets. If these claims hold, practitioners can choose HSTU-like backbones when they expect to deepen models, and they can retrofit legacy sequential models with the same modules to restore scalability.

What carries the argument

The central object is the HSTU block, a transformer-style recommendation transducer that substitutes SiLU for Softmax in attention weighting, adds relative position and time-difference bucket biases to attention scores, and uses a residual connection placed around the pre-normalized sublayers plus a pointwise feature-interaction layer. The paper isolates the scaling law's origin by sweeping block count, ablating one component at a time, and surgically transferring the residual and relative-bias modules into SASRec, showing that these two modules, rather than raw parameter count, are what carry scalability.

What would settle it

Run the same 2-to-32 block depth sweep on a production-scale behavior log with sequence lengths above 1000 and embedding dimensions above 1000, comparing HSTU, Llama, GPT, and SASRec with and without the transplanted residual and relative-bias modules. If HSTU's recall or ranking curves plateau or decline while SASRec with the transplanted modules continues to improve, or if removing the relative attention bias no longer flattens the HSTU curve, the paper's claim about the origin and transferability of the scaling law would be falsified.

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Extended reading notes

Core claim

The paper's central claim is that model depth produces a scaling law only for certain recommendation architectures. In controlled sweeps from 2 to 32 blocks on the public rating and review datasets, HSTU and Llama improve or hold their recall metrics, whereas GPT and SASRec degrade sharply, sometimes to near-random levels. Ablations that remove one component at a time show that the relative attention bias, especially the bucketed time-difference part, is what keeps the curve rising, and that the residual-connection pattern used by HSTU and Llama is more robust than SASRec's post-normalization residual; combining Llama-style residuals with the relative attention bias turns SASRec from non-scaling into scaling. The paper also presents the first evaluation of HSTU on ranking tasks, where it improves with depth while Llama does not, and where larger numbers of negative samples help rather than hurt, provided the model size and embedding dimension are matched to the dataset.

Load-bearing premise

The scaling trends measured on public datasets with embedding sizes between 50 and 400 and sequence lengths up to 200 transfer to trillion-parameter production systems with much longer user sequences; the paper never validates on a production-scale model or dataset.

Editorial extensions

If this is right

  • Architecture choice determines whether adding depth helps at all: HSTU- and Llama-style backbones are the ones worth scaling vertically in recall, while GPT and SASRec backbones will not reward extra blocks.
  • HSTU's scaling extends beyond recall: on ranking tasks it improves with depth, so the same backbone can serve both the recall and ranking stages of a recommendation pipeline.
  • Legacy sequential recommenders can be made scalable by transplanting the residual connection pattern and relative attention bias, offering an upgrade path for existing systems without changing the whole architecture.
  • Model size must be matched to dataset size: the optimal number of blocks shrinks as embedding size grows, and larger sequence lengths justify larger models, so a fixed large model is not universally best.
  • Generative ranking models benefit from using more negative samples rather than aggressive subsampling, which suggests that curation for ranking should preserve negatives at scale.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Going beyond the paper: if the depth-scaling curve is governed by the time-bucket attention bias rather than by parameter count, then models trained on temporally regular logs should scale faster than models on sparse, irregular logs; this can be tested by changing time-bucket granularity while holding depth fixed.
  • The paper reports a near-constant product of optimal layers and embedding dimension but does not fit a power-law exponent; fitting the public-data curves to a two-exponent power law could give a practical rule for choosing depth and embedding size before training.
  • Because GPT-style backbones fail to scale in recommendation despite scaling well in language, the missing ingredient may be recommendation-specific inductive biases; a direct test would be to add the same relative time-bias and residual pattern to a GPT-style backbone and rerun the block sweep.
  • The negative-sampling result suggests that generative ranking models can exploit the full negative distribution, which would change how industrial click-through-rate datasets are curated if the effect transfers to production scale.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. This paper presents an empirical study of scaling behavior in transformer-based recommendation models, with a focus on Meta's HSTU architecture. Using public datasets (ML-1M, ML-20M, AMZ-Books, CIKM, IJCAI, AMZ-MD), the authors compare HSTU, Llama, GPT, and SASRec as the number of attention blocks grows, concluding that HSTU and Llama exhibit better scalability while GPT and SASRec do not. They then ablate HSTU components (relative attention bias, SiLU activation, feature interaction), run parameter analyses over embedding dimension, number of heads, sequence length, and depth, and test HSTU on complex user behavior (side information, multi-behavior, multi-domain) and ranking tasks. The paper claims to identify residual connection patterns and relative attention bias as the origins of the scaling law and states that it is the first to evaluate HSTU on ranking tasks. The experiments are broad but rely on single-run evaluations and fixed hyperparameters for baselines, which weakens the central comparative claims.

Significance. If the conclusions were robustly supported, this would be a useful and timely contribution: the paper asks an important question about whether recommendation backbones scale with depth, provides a wide range of experiments across six datasets and multiple tasks, and includes ablations that isolate HSTU-specific components. The first evaluation of HSTU on ranking tasks is also practically relevant. A particular strength is the explicit attempt to attribute scaling behavior to observable architectural choices rather than treating HSTU as a black box. The paper states that supplementary code is available on GitHub, which is a valuable reproducibility commitment. However, the central comparative claim (HSTU and Llama scale, GPT and SASRec do not) is currently undermined by optimization confounds and by the absence of uncertainty quantification, so the significance is conditional on the core experiments being reworked.

major comments (5)
  1. [§5.2, Table 1] The conclusion that GPT and SASRec "show no scalability" conflates architectural scaling capacity with optimization stability under fixed hyperparameters. Section 5.1.4 states that original implementations' hyperparameters were kept fixed except for depth. Under this protocol, deeper GPT and SASRec runs collapse to near-random metrics (e.g., GPT on ML-1M drops from HR@10 0.2803 at 4 blocks to 0.0353 at 8 blocks; SASRec on ML-20M drops from 0.2781 at 2 blocks to 0.0599 at 4 blocks). Such a collapse is the classic signature of training instability under an unadjusted learning rate or warmup schedule, not evidence of an absent scaling law. The comparison should be repeated with per-depth hyperparameter tuning or with training-convergence diagnostics to separate architecture scaling from trainability.
  2. [§5.2, Table 1; §5.5.2, Table 14] The evidence that HSTU scales reliably is itself not statistically supported. Table 1 shows HSTU HR@10 on AMZ-Books falling from 0.0680 at 16 blocks to 0.0584 at 32 blocks, and on ML-1M the 16-to-32-block change is only 0.3322 to 0.3298. In the ranking task, Table 14 shows ML-20M AUC 0.7992 at 24 blocks dropping to 0.7914 at 32 blocks. Since no standard deviations, multiple seeds, or significance tests are reported anywhere in Section 5, the paper does not establish that HSTU scales more consistently than the baselines; several of the differences driving the qualitative conclusions are within a range that could easily be noise in single-run evaluations.
  3. [§5.3.2, Table 6] The claim that "the product of the optimal number of layers (L) and embedding dimension (D) remains constant, supporting our theoretical model that size is proportional to O(LD)" is not supported by the reported data. Reading the NDCG@10 columns for |S|=100, the apparent optimal depth is around 64 blocks for D=50 (LD=3200), 32 for D=100 (LD=3200), 24 for D=200 (LD=4800), and 32 for D=400 (LD=12800). The product is not constant, and the NDCG differences among neighboring depths are typically a few thousandths, so the notion of a well-defined optimum is fragile. This section should quantify the claimed relationship or reframe it as a qualitative observation.
  4. [§5.3.3, Table 8] The attribution of HSTU's scaling law to the residual connection pattern and relative attention bias is overreaching. The experiments modify SASRec by adopting the residual structure of HSTU or Llama and adding relative attention bias; these are precisely the architectural changes known to improve trainability of deep transformers, so the results are consistent with an optimization-stability explanation rather than a fundamental scaling-law origin. Moreover, even the best modified SASRec reaches only HR@10 0.3182 at 32 blocks on ML-20M, well below HSTU's 0.3569 (Table 1), so the modified model does not reproduce HSTU's scaling behavior. The wording should be softened to "factors associated with improved scaling" unless additional experiments single out the causal mechanism.
  5. [§5.5.3, Table 15] The text states that increasing the negative sampling ratio "leads to a continuous improvement in the model's performance," but the table is non-monotonic. For HSTU on ML-20M, AUC at ratio 0.4 (0.7952) is higher than at ratio 0.8 (0.7899) and at ratio 1.0 (0.7916); on AMZ-Books, AUC drops from 0.7338 at ratio 0.8 to 0.7037 at ratio 1.0. The claim should be revised to describe the observed pattern, or the experiment should be rerun with multiple seeds to determine whether the fluctuations are within noise.
minor comments (6)
  1. [§5.1.1, §5.1.4] The KuaiRand-27k dataset is listed in Section 5.1.1 but never appears in any reported experiment; it should either be used or removed from the list.
  2. [§5.1.4] The paper says that original implementations' hyperparameters were kept fixed, but it does not list the actual learning rate, schedule, batch size, warmup, or dropout for each backbone; a reproducibility appendix should report these details.
  3. [§5.3.4, Figure 4] The t-SNE visualization is used to infer that "a well-normalized model may enhance both scalability and recall performance," but no quantitative measure of clustering or normalization is provided, so the conclusion is not supported by the figure alone.
  4. [§5.5.1] The notation for the ranking task is confusing: the input sequence uses b′_i for labels, but the loss uses both b″_k and b′_k; the text should clearly define the ground-truth label and the prediction (e.g., y_i and ŷ_i).
  5. [Table 2] The table caption says "Impact of various HSTU components on scaling law" but the row labels use the abbreviation "r.a.b." without defining it in the caption or text; the abbreviation should be expanded at first use.
  6. [§6.3 and References] There is a duplicated in-text citation "[2, 40, 48, 48, 66, 68]" for efficiency optimizations, and reference [99] has missing venue and year details; these should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are empirical measurements on public benchmarks, with no parameter fitted and renamed as a prediction.

full rationale

The paper's central claims concern how recall and ranking performance change as the number of transformer blocks increases for HSTU, Llama, GPT, and SASRec. These claims are supported by reported measurements in Tables 1, 8, and 14 on public datasets, rather than by a derivation in which one quantity is defined in terms of another. Section 5.1.4 states that original model hyperparameters were kept fixed except for depth; this makes the depth-collapse of GPT and SASRec an experimental outcome, not a quantity forced by construction. The scaling-law origin analysis in Section 5.3.3 modifies SASRec by adding relative attention bias and HSTU/Llama-style residual patterns, then measures the effect; the conclusion is induced from Table 8 rather than presupposed. The reliance on HSTU [126] is an external citation to prior work by other authors, and no uniqueness theorem or ansatz is imported through self-citation. Any concern that fixed hyperparameters make the GPT/SASRec comparison an optimization-collapse artifact is a validity or correctness question, not a circularity in the sense defined here. The paper is therefore self-contained against external benchmarks, and no circular step can be exhibited.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new theoretical entities are introduced. The claims rest on standard transformer machinery and on domain assumptions about dataset representativeness and implementation fidelity, plus one ad hoc regularity (O(LD)) that is asserted without proof.

free parameters (2)
  • Per-dataset embedding dimension = ML-1M: 50, ML-20M: 256, AMZ-Books: 64; reduced to 4 in Table 17
    Embedding size is a manually chosen hyperparameter per dataset; the observed scaling behavior depends on this choice, and the paper shows that reducing it changes ranking performance.
  • Maximum sequence length = up to 200 for parameter analyses (|S| = 100 and 200 in Table 6)
    Sequence length is set by preprocessing and varied in experiments; the paper notes that longer sequences do not always help, so the chosen lengths affect the scaling conclusions.
assumptions (4)
  • domain assumption Public benchmark datasets such as MovieLens, Amazon, CIKM, and IJCAI are representative of large-scale industrial recommendation data.
    The paper generalizes 'scaling laws of large recommendation models' from datasets whose largest interaction count is about 20 million, while industrial systems handle trillions of interactions; no production-scale validation is included (Sections 5.1.1 and 5.3.2).
  • domain assumption The reproduced HSTU model faithfully corresponds to the original HSTU of [126] and the ranking head does not distort the base model.
    Section 5.1.4 states that original hyperparameters are maintained, but the paper does not compare its recall numbers against those reported in [126], so reproduction fidelity is unverified.
  • domain assumption Varying only the number of attention blocks while fixing all other hyperparameters isolates the effect of model depth on scaling.
    This is the experimental design for RQ1 and RQ2 (Section 5.2); if optimal learning rate, regularization, or sequence length differ per depth, the observed scaling laws could be artifacts.
  • ad hoc to paper The optimal model size is proportional to O(LD), where L is the number of layers and D is the embedding dimension.
    Stated in Section 5.3.2 as 'supporting our theoretical model' but no derivation is given; it is a post-hoc regularity inferred from Table 6.

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Pith. "Pith review of Scaling New Frontiers: Insights into Large Recommendation Models." pith.science (2026). https://pith.science/paper/SXLQVYXJ

@misc{pith2026241200714,
  author       = {Pith},
  title        = {Pith review of: Scaling New Frontiers: Insights into Large Recommendation Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SXLQVYXJ}},
  note         = {Machine review of arXiv:2412.00714}
}
read the original abstract

Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate increasingly large embedding tables, scaling up to tens of terabytes for industrial use. However, the expansion of network parameters in traditional recommendation models has plateaued at tens of millions, limiting further benefits from increased embedding parameters. Inspired by the success of large language models (LLMs), a new approach has emerged that scales network parameters using innovative structures, enabling continued performance improvements. A significant development in this area is Meta's generative recommendation model HSTU, which illustrates the scaling laws of recommendation systems by expanding parameters to thousands of billions. This new paradigm has achieved substantial performance gains in online experiments. In this paper, we aim to enhance the understanding of scaling laws by conducting comprehensive evaluations of large recommendation models. Firstly, we investigate the scaling laws across different backbone architectures of the large recommendation models. Secondly, we conduct comprehensive ablation studies to explore the origins of these scaling laws. We then further assess the performance of HSTU, as the representative of large recommendation models, on complex user behavior modeling tasks to evaluate its applicability. Notably, we also analyze its effectiveness in ranking tasks for the first time. Finally, we offer insights into future directions for large recommendation models. Supplementary materials for our research are available on GitHub at https://github.com/USTC-StarTeam/Large-Recommendation-Models.

Figures

Figures reproduced from arXiv: 2412.00714 by the authors.

Figure 1
Figure 1. Illustration of the shift in modeling paradigms. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The figure outlines the structure of the paper, starting with the shift in user behavior modeling paradigms discussed [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The research questions addressed in experiments. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Visualization of user embeddings on the ML-20M [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Comparison of the scalability performance between [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Illustration of scoring network architectures. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.