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LLM mid-training data composition impacts alignment, research finds

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the optimal data composition during the mid-training phase of large language models, finding that a moderate band of data (10%-40%) is best for all domains. The study, which used the Qwen3-8B-Base model and the KOR-Bench dataset, also revealed that these domain-specific gaps in performance persist even after a subsequent alignment pass. Furthermore, the research indicated that completely omitting a domain during mid-training leads to a collapse in accuracy, though this effect is partially commingled with general model drift. AI

IMPACT Optimal data composition during mid-training is crucial for model performance and alignment, suggesting a need for more principled data selection strategies.

RANK_REASON The cluster contains a research paper detailing findings on LLM training methodology.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM mid-training data composition impacts alignment, research finds

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yunpeng Xu, Kun Zheng ·

    Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training

    arXiv:2609.09081v1 Announce Type: new Abstract: Mid-training, the stage between pre-training and alignment, is where a model's per-domain data composition is typically set by data availability rather than principled design. We ask what that decision buys, and whether a later alig…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training

    Mid-training, the stage between pre-training and alignment, is where a model's per-domain data composition is typically set by data availability rather than principled design. We ask what that decision buys, and whether a later alignment pass can undo it. In a controlled logical-…

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