The Problem of Knowledge Choice in LLM Pretraining
Growing massive language fashions entails substantial computational funding, particularly when experimenting with various pretraining corpora. Evaluating datasets at full scale—on the order of billions of parameters and tons of of billions of tokens—can eat tons of of hundreds of GPU hours per run. Consequently, practitioners resort to smaller‐scale experiments as proxies for big‐mannequin habits. But these “pilot” research are hardly ever revealed, producing a fragmented panorama by which every laboratory repeats related small‐scale exams with out shared benchmarks or methodologies . This opacity impedes reproducibility, underutilizes collective insights, and obscures the true commerce‑offs between growth compute and ultimate mannequin efficiency.
DataDecide
To handle these limitations, the Allen Institute for AI (AI2), in collaboration with the College of Washington and the College of Pennsylvania, at present releases DataDecide—a complete suite of managed pretraining experiments spanning 25 distinct corpora and 14 mannequin sizes from 4 million to 1 billion parameters. DataDecide’s datasets embody effectively‑recognized sources similar to Dolma, DCLM, RefinedWeb, C4, and FineWeb, alongside variations produced by area ablation, deduplication, high quality filtering, and supply mixing. Every mannequin is educated at a hard and fast token‑to‑parameter ratio of 100 (100 tokens per parameter), reflecting the “overtraining” regime that optimizes inference effectivity. In complete, over 1,050 fashions and greater than 30,000 checkpoints—every evaluated throughout ten downstream duties—are launched to the general public.
Technical Construction and Pragmatic Advantages
DataDecide orchestrates experiments alongside three axes:
- Knowledge Recipes: Twenty‑5 effectively‑documented pretraining corpora, every embodying completely different curation methods (see Desk 1 within the paper for full recipe specs) .
- Mannequin Scale: Fourteen parameter configurations (4 M–1 B), programmatically derived through the OLMo mannequin ladder to make sure constant coaching hyperparameters throughout scales. Every non‑goal scale contains two “early‑cease” seed runs, whereas the 1 B‑parameter fashions characteristic three full seed reruns to quantify variability.
- Analysis Suite: The OLMES benchmark of ten a number of‑selection duties (e.g., MMLU, ARC Simple/Problem, HellaSwag, MBPP, HumanEval) supplies a multifaceted view of language understanding, commonsense reasoning, and code era efficiency.
By releasing each pretraining datasets and corresponding fashions, DataDecide permits researchers to:
- Reuse checkpoints for brand new evaluations with out retraining.
- Experiment with novel prediction strategies (e.g., superior scaling‑regulation suits, smoothing strategies).
- Examine benchmark sensitivity to coaching information and mannequin scale.
Key Findings and Quantitative Insights
DataDecide’s systematic evaluation yields 4 sensible pointers:
- Single‑Scale Baseline Robustness: Rating corpora by downstream accuracy at a single, small scale (e.g., 150 M parameters) achieves ~80 p.c resolution accuracy for predicting one of the best dataset on the 1 B‑parameter goal scale. In distinction, eight baseline scaling‑regulation extrapolations don’t surpass this easy heuristic, underscoring its price‑effectiveness.
- Job‑Dependent Compute Sensitivity: The compute funds required for dependable choices varies markedly by activity. Benchmarks like MMLU and ARC Simple change into predictable with lower than 0.01 p.c of the goal compute, whereas HellaSwag and SocialIQA demand orders of magnitude extra FLOPs to realize related resolution accuracy .
- Proxy Metric Choice: Steady probability metrics—particularly the character‑normalized common likelihood of appropriate continuations (CORRECT PROB) and complete likelihood (TOTAL PROB)—outperform discrete accuracy measures at small scales. That is most pronounced on code duties (MBPP, HumanEval), the place resolution accuracy jumps from close to‑random to over 80 p.c with CORRECT PROB because the proxy .
- Variance and Unfold Issues: Excessive resolution accuracy correlates with low run‑to‑run variance (noise) and ample efficiency unfold throughout datasets. Proxy metrics that cut back noise or amplify unfold thus immediately improve prediction reliability.
Concluding Perspective
DataDecide transforms pretraining information choice from an advert hoc artwork right into a clear, information‐pushed science. By open‑sourcing all 25 corpora, 1,050 fashions, 30,000+ checkpoints, and analysis scripts on Hugging Face and GitHub, AI2 invitations the group to breed findings, prolong evaluations to new benchmarks, and innovate on resolution‑making strategies. As LLM growth continues to demand ever‑higher compute sources, DataDecide gives a principled framework for minimizing wasted experiments and maximizing perception—paving the best way towards extra environment friendly, reproducible, and collaborative AI analysis.
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