Data and Evaluation Closed-Loop for Model Capability Enhancement
A new method called the 'capability slice' bridges the gap between evaluation and data in LLM pre-training, enabling targeted data interventions from benchmark failures. Tested on two case studies, it correctly distinguished a masked loss artifact from a genuine data issue.
[2606.28471] Data and Evaluation Closed-Loop for Model Capability Enhancement
[Submitted on 26 Jun 2026]
Title:Data and Evaluation Closed-Loop for Model Capability Enhancement
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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, not method. We close this gap with the \emph{capability slice}: a group of evaluation samples sharing background condition, task type, solving operation, and output constraint -- precise enough to localize a single weakness yet stable enough to survive aggregation, unlike a benchmark name, too coarse, or a single sample, too noisy. Built around this unit, an evaluation taxonomy, a non-instruction data taxonomy, and mapping rules form a closed loop turning a benchmark-level failure into a targeted, testable data intervention. We test this loop on two case studies pulling in opposite directions. First, the loop rules the data out: continued pre-training drives BBH down by $-46.82\%$, but diagnosis traces this to a single masked \texttt{\textless EOS\textgreater} loss rather than weakened reasoning; restoring it recovers BBH to $66.44$, above the original checkpoint, without changing the data. Second, the loop rules the data in: a persistent math-reasoning weakness is decomposed by solving operation into specific failing combinations, and a weakness-targeted sampling procedure built from it lifts AIME2025/AIME2026 Pass@128 from $6.67$/$0.00$ to $26.67$ each. The same unmodified loop reaches opposite, correct verdicts in both cases, showing the evaluation-to-data inference can be routine, auditable, and experimentally validated rather than intuitive.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.28471 [cs.AI]
(or arXiv:2606.28471v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.28471
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhixuan Li [view email] [v1] Fri, 26 Jun 2026 14:45:57 UTC (1,600 KB)
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