We present histograms depicting distribution of cluster sizes in all the datasets (see Fig. 7-11). Please, note that all the figures are in log-log scale. We see a significant drop in the number of clusters starting from the size of around 100. This drop is present both in DCLM and FineWeb-Edu2 (see Fig. 8 and 9 respectively), and most likely is explained by a combination of the deduplication strategy and quality when creating both datasets: DCLM deduplication was done individually within 10 shards, while FineWeb-Edu2 was deduplicated within every Common Crawl snapshot. We find that large clusters usually contain low quality material (repeated advertisements, license agreements templates, etc), so it’s not surprising that such documents were removed. Notably, DCLM still contained one cluster with the size close to 1 million documents, containing low quality documents seemingly coming from the advertisements (see Appendix).We find both Zyda-1and Dolma-CC contain a small amount of duplicates, which is expected, since both datasets were deduplicated globally by their authors. Remaining duplicates are likely false negatives from the initial deduplication procedure. Note, that distribution of duplicates clusters sizes of these two datasets (Fig. 10 and 11) don’t contain any sharp drops, but rather hyper exponentially decreases with cluster size.
Below is an example of the document from the largest cluster (~1M documents) of duplicates in DCLM (quality score 0.482627):
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Below one will find a few documents with different quality scores from DCLM coming from the same duplicates cluster. Quality score varies from ~0.2 to ~0.04.
Reported scores underlined.
Pass@1 scores with greedy sampling.
Pass@1 scores with greedy sampling. Livebench 2024-11-25.
Bold: Best score at 1.5B scale w/ greedy sampling
*reported scores
Evals (reported underlined). All numbers pass@1 estimated using n=16
Footnote: Training on the Eurus-2-RL dataset did not match the DeepScaleR math evaluation numbers, possibly due to lower quality synthetic math questions in NuminaMath-CoT providing a mixed training signal, or the solvability filtering process with QwQ-preview reducing the difficulty of the dataset. Additionally, the relatively small percentage of code (5%) likely led to math dominating training at the expense of code performance. Training on domain specific datasets and merging resulting models seems to be a potential way to counteract this problem, as demonstrated with SFT in Light-R1.
