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pith:RVO2FDVK

pith:2025:RVO2FDVKSC2IBFVSEY3I6MDCOW
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Predictable Scale: Part I, Step Law -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining

Daxin Jiang, Hanshan Zhang, Haoying Wang, Houyi Li, Ning Ding, Qiufeng Wang, Shijie Xuyang, Shuigeng Zhou, Wenzhen Zheng, Xiangyu Zhang, Yuantao Fan, Zhenyu Ding, Zili Wang

arxiv:2503.04715 v7 · 2025-03-06 · cs.LG · cs.AI

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Cited by

12 papers in Pith

How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training
Staged Factorial Screening for Budget-Constrained Micro-Pretraining
On the Nonlinearity of Learning Rate Scaling for LLM Training
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First computed 2026-07-05T11:55:38.652806Z
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Schema pith-number/v1.0

Canonical hash

8d5da28eaa90b48096b226368f30627596f9c6b4bab292c77906312463a550a6

Aliases

arxiv: 2503.04715 · arxiv_version: 2503.04715v7 · doi: 10.48550/arxiv.2503.04715 · pith_short_12: RVO2FDVKSC2I · pith_short_16: RVO2FDVKSC2IBFVS · pith_short_8: RVO2FDVK
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Verify this Pith Number yourself 
curl -sH 'Accept: application/ld+json' https://pith.science/pith/RVO2FDVKSC2IBFVSEY3I6MDCOW \
  | 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: 8d5da28eaa90b48096b226368f30627596f9c6b4bab292c77906312463a550a6
Canonical record JSON 
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    "abstract_canon_sha256": "1298849a5e10ac3eaa2d2bcc18406fbfb6243a2dc61fb83235afe79c478ebdb0",
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      "cs.AI"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-03-06T18:58:29Z",
    "title_canon_sha256": "a7dba12f2962e8e9d2dea8ac51043baf03853d406a826d2a5d6faab1eb024029"
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