Farseer: A Refined Scaling Law in Large Language Models
Paper • 2506.10972 • Published • 1
Datasets:
text stringlengths 0 148 |
|---|
Apache License |
Version 2.0, January 2004 |
http://www.apache.org/licenses/ |
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION |
1. Definitions. |
"License" shall mean the terms and conditions for use, reproduction, |
and distribution as defined by Sections 1 through 9 of this document. |
"Licensor" shall mean the copyright owner or entity authorized by |
the copyright owner that is granting the License. |
"Legal Entity" shall mean the union of the acting entity and all |
other entities that control, are controlled by, or are under common |
control with that entity. For the purposes of this definition, |
"control" means (i) the power, direct or indirect, to cause the |
direction or management of such entity, whether by contract or |
otherwise, or (ii) ownership of fifty percent (50%) or more of the |
outstanding shares, or (iii) beneficial ownership of such entity. |
"You" (or "Your") shall mean an individual or Legal Entity |
exercising permissions granted by this License. |
"Source" form shall mean the preferred form for making modifications, |
including but not limited to software source code, documentation |
source, and configuration files. |
"Object" form shall mean any form resulting from mechanical |
transformation or translation of a Source form, including but |
not limited to compiled object code, generated documentation, |
and conversions to other media types. |
"Work" shall mean the work of authorship, whether in Source or |
Object form, made available under the License, as indicated by a |
copyright notice that is included in or attached to the work |
(an example is provided in the Appendix below). |
"Derivative Works" shall mean any work, whether in Source or Object |
form, that is based on (or derived from) the Work and for which the |
editorial revisions, annotations, elaborations, or other modifications |
represent, as a whole, an original work of authorship. For the purposes |
of this License, Derivative Works shall not include works that remain |
separable from, or merely link (or bind by name) to the interfaces of, |
the Work and Derivative Works thereof. |
"Contribution" shall mean any work of authorship, including |
the original version of the Work and any modifications or additions |
to that Work or Derivative Works thereof, that is intentionally |
submitted to Licensor for inclusion in the Work by the copyright owner |
or by an individual or Legal Entity authorized to submit on behalf of |
the copyright owner. For the purposes of this definition, "submitted" |
means any form of electronic, verbal, or written communication sent |
to the Licensor or its representatives, including but not limited to |
communication on electronic mailing lists, source code control systems, |
and issue tracking systems that are managed by, or on behalf of, the |
Licensor for the purpose of discussing and improving the Work, but |
excluding communication that is conspicuously marked or otherwise |
designated in writing by the copyright owner as "Not a Contribution." |
"Contributor" shall mean Licensor and any individual or Legal Entity |
on behalf of whom a Contribution has been received by Licensor and |
subsequently incorporated within the Work. |
2. Grant of Copyright License. Subject to the terms and conditions of |
this License, each Contributor hereby grants to You a perpetual, |
worldwide, non-exclusive, no-charge, royalty-free, irrevocable |
copyright license to reproduce, prepare Derivative Works of, |
publicly display, publicly perform, sublicense, and distribute the |
Work and such Derivative Works in Source or Object form. |
3. Grant of Patent License. Subject to the terms and conditions of |
this License, each Contributor hereby grants to You a perpetual, |
worldwide, non-exclusive, no-charge, royalty-free, irrevocable |
(except as stated in this section) patent license to make, have made, |
use, offer to sell, sell, import, and otherwise transfer the Work, |
where such license applies only to those patent claims licensable |
by such Contributor that are necessarily infringed by their |
Contribution(s) alone or by combination of their Contribution(s) |
with the Work to which such Contribution(s) was submitted. If You |
institute patent litigation against any entity (including a |
cross-claim or counterclaim in a lawsuit) alleging that the Work |
or a Contribution incorporated within the Work constitutes direct |
or contributory patent infringement, then any patent licenses |
granted to You under this License for that Work shall terminate |
as of the date such litigation is filed. |
4. Redistribution. You may reproduce and distribute copies of the |
Work or Derivative Works thereof in any medium, with or without |
modifications, and in Source or Object form, provided that You |
meet the following conditions: |
(a) You must give any other recipients of the Work or |
Derivative Works a copy of this License; and |
(b) You must cause any modified files to carry prominent notices |
stating that You changed the files; and |
(c) You must retain, in the Source form of any Derivative Works |
End of preview. Expand in Data Studio
Public-checkpoint reanalysis: normalised run tables
Normalised training-loss observations from published language-model studies, used for the public-checkpoint reanalysis accompanying Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation (NeurIPS 2026). Every number in that analysis is recomputable from these tables, with no need to re-fetch the upstream sources.
Files
| File | Rows | Contents |
|---|---|---|
runs_expanded.csv |
14,065 | Every observation of the eleven sources below, mid-training checkpoints included |
runs_normalised.csv |
2,215 | Terminal observations of four sources: Chinchilla isoFLOP, Muennighoff et al., Farseer and Gadre et al. |
datadecide_ppl_by_group.csv |
29,665 | Unmodified copy of DataDecide's perplexity_metrics_by_group.csv (Apache-2.0) |
Schema of the run tables
cell (source/corpus), run_id, N (non-embedding parameters), D (training tokens), loss,
loss_smooth, loss_val where released, corpus (E: the final loss of a fully trained run, 2,446 rows;
X: a mid-training checkpoint, 11,619 rows), and k = D/N (tokens per parameter).
Sources and licenses
Each row keeps the license of the source it comes from. The rows are a subset of each source's
columns, renamed and normalised to the schema above (a change, in the sense of Apache-2.0
section 4(b)); datadecide_ppl_by_group.csv is unchanged. Our own normalisation and the k
column are released under CC-BY-4.0. License texts are in LICENSES/.
cell prefix |
Rows | Source | License | Notice |
|---|---|---|---|---|
chinchilla_isoflop |
245 | Hoffmann et al. 2022, digitized by Besiroglu et al. 2024 (Epoch AI), via ColPret (Choshen et al. 2024) | CC-BY-4.0 (Epoch AI); MIT (ColPret) | LICENSES/CC-BY-4.0.txt, LICENSES/MIT-ColPret.txt |
datablations |
1,462 | Muennighoff et al. 2023, huggingface/datablations | Apache-2.0 | LICENSES/Apache-2.0.txt |
datadecide |
10,551 | Magnusson et al. 2025, allenai/DataDecide | Apache-2.0 | LICENSES/Apache-2.0.txt |
farseer |
404 | Li et al. 2025, Farseer | none stated | see below |
gadre_overtrain |
104 | Gadre et al. 2024, mlfoundations/scaling, via ColPret | MIT | LICENSES/MIT-mlfoundations-scaling.txt, LICENSES/MIT-ColPret.txt |
gemstones_cooldown, gemstones_hot |
199, 264 | McLeish et al. 2025, gemstone-scaling-laws | MIT | LICENSES/MIT-gemstone-scaling-laws.txt |
gpt3 |
97 | Brown et al. 2020, digitized curves via ColPret | MIT (ColPret) | LICENSES/MIT-ColPret.txt |
mad |
648 | Poli et al. 2024, mad-lab, via ColPret | MIT | LICENSES/MIT-mad-lab.txt, LICENSES/MIT-ColPret.txt |
olmo_ladder |
32 | Bhagia et al. 2024, allenai/OLMo-ladder | Apache-2.0 | LICENSES/Apache-2.0.txt |
opt_trajectories |
59 | Xia et al. 2023 (OPT, Zhang et al. 2022), via ColPret | MIT (ColPret); upstream none stated | LICENSES/MIT-ColPret.txt |
Farseer. Its repository states no license, so this dataset does not hold its raw run table.
Its 404 rows here keep only N, D and the three loss columns, cited to Li et al. 2025
(arXiv:2506.10972); the original is at
https://github.com/Farseer-Scaling-Law/Farseer/blob/main/ipynb/data/1222_full.csv, and the
analysis code downloads it from there.
Reproducing
The analysis code, the pre-registered protocol with its dated amendments, and the screening log
are in https://github.com/lab-v2/tpp-coverage (camera_ready/public_checkpoints). Point the
analysis at these tables in place of its fetch step.
Acknowledgments
This research was funded by ARO grant W911NF-24-1-0007. Separately, this work used resources at NCSA through allocation CIS230318 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program (Boerner et al., 2023), which is supported by U.S. National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296. Three AMD EPYC CPU chips, donated by AMD, were used for the experiments. This research was supported in part through computational resources provided by Syracuse University (NSF award ACI-1341006).
- Downloads last month
- 179