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pith:TZ247IAZ

pith:2026:TZ247IAZLYSTS5Z5KH6Q2BCIY6

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TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation

Hengfeng Wang, Huichao Chai, Lu Xu, Maojun Peng, Shiqing Fan, Wei Guo, Xuemiao Li, Yaoyuan Wang, Yibo Jin, Yongxiang Feng, Zhixin Wu

TurboGR enables training of up to 0.2 billion parameter generative recommendation models on Ascend NPUs at 54.71 percent MFU with 0.97 scalability.

arxiv:2605.13433 v1 · 2026-05-13 · cs.DC · cs.LG

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Claims

C1strongest claim

Evaluated on the KuaiRand-27K dataset, TurboGR supports training at up to 0.2B parameters and achieves 54.71% MFU with near-linear scalability (0.97).

C2weakest assumption

The semi-asynchronous training and jagged optimizations preserve model quality and convergence while the reported MFU and scalability numbers generalize beyond the specific KuaiRand-27K setup and Ascend hardware configuration.

C3one line summary

TurboGR trains up to 0.2B-parameter generative recommendation models on Ascend NPUs at 54.71% MFU with 0.97 near-linear scalability via jagged acceleration, hierarchical parallelism, and negative sampling optimizations.

References

21 extracted · 21 resolved · 1 Pith anchors

[1] Personalized news recommendation: Methods and challenges.ACM Transactions on Information Systems, 41(1):1–50, 2023 2023

[2] Analysis of recommendation algorithms for e-commerce 2000

[3] Analyzing user engagement with tiktok’s short format video recommendations using data donations 2024

[4] Item-based collaborative filtering recommen- dation algorithms 2001

[5] Self-attentive sequential recommendation 2018

Receipt and verification

First computed	2026-05-18T02:44:47.145484Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9e75cfa0195e2539773d51fd0d0448c791ac3db91fa37ffca6363a2af1e6d59f

Aliases

arxiv: 2605.13433 · arxiv_version: 2605.13433v1 · doi: 10.48550/arxiv.2605.13433 · pith_short_12: TZ247IAZLYST · pith_short_16: TZ247IAZLYSTS5Z5 · pith_short_8: TZ247IAZ

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/TZ247IAZLYSTS5Z5KH6Q2BCIY6 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9e75cfa0195e2539773d51fd0d0448c791ac3db91fa37ffca6363a2af1e6d59f

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
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    "submitted_at": "2026-05-13T12:26:29Z",
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