arXiv:2606.28471v1 Announce Type: new Abstract: Model capability is the central variable in LLM pre-training, yet is never observed directly: data shapes it prospectively, while evaluation reveals it only retrospectively, compressing samples, prompts, decoding, and scoring rules into one noisy score. Practical optimization runs this backward: a failure is observed first, and the engineer must infer the corpus fix. The two sides speak incompatible vocabularies -- benchmark names and per-sample correctness versus data sources, domains, and quality labels -- so this inference is usually intuition
The increasing scale and complexity of LLMs necessitate more rigorous and systematic approaches to model development and evaluation, moving beyond intuition-based fixes.
This work introduces a foundational framework for optimizing LLM capabilities by closing the loop between data selection, model training, and performance evaluation, leading to more efficient and predictable AI development.
The development process for LLMs shifts from an intuitive, retrospective debugging cycle to a more data-driven, prospective optimization pipeline, potentially accelerating breakthroughs and reducing development costs.
- · AI developers
- · Large Language Model companies
- · Data science platforms
- · Companies relying on intuition-based model tuning
- · Inefficient LLM development pipelines
More robust and capable LLMs will be developed with greater speed and less waste.
The ability to systematically enhance model capabilities could lead to new applications and markets currently too challenging for existing models.
This optimized development process could further centralize LLM development expertise among those with the best data and evaluation infrastructure.
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Read at arXiv cs.AI