A Comprehensive Framework for All Stages of Large-Model Training: From Data and Tokenization, through Pretraining, to Post-Training Alignment

Authors: Xiangjing An , Zhibin Jiao , Aiting Chen

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Abstract: The capabilities of large language models (LLMs) and multimodal LLMs (MLLMs) arise from the synergy of the full training pipeline, including data and tokenization, pretraining, and post-training (supervised fine-tuning, preference alignment, reinforcement learning, and continual optimization), rather than any individual stage. Existing surveys mostly adopt a single-stage or single-technique perspective, treating related research as isolated systems without unified stage division or a horizontally comparable basis.

This paper proposes a unified stage division and a six-dimensional analytical framework covering data, objective function, optimization strategy, system constraints, evaluation, and risk and governance. We systematically review 395 core references from 2017 to 2026, and present a conceptual map of cross-stage effects: forward constraints of pretraining on post-training, and reverse empowerment of post-training on data and pretraining.

We derive three core findings. First, the comparability of scaling laws depends on data quality and mixture, a long-neglected variable. Second, preference alignment methods (RLHF, DPO) and reasoning reinforcement learning with verifiable rewards redefine post-training boundaries, while the Pass@1 versus Pass@K debate remains unresolved. Third, multimodal training poses structural challenges to the pure-text paradigm in tokenizer design, training objectives, and evaluation.

The paper provides a cross-stage effects diagram, a method-family map, a practical decision tree, and at least 10 verifiable open problems, with an extensible skeleton for focused sub-surveys per stage. As of the retrieval date, peer-reviewed evidence (A+B) accounts for approximately 79% of the 395 references.

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.33451 [cs.AI]

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arXiv:2610.33451v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33451

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: An Xiangjing [ view email ]

[v1]

Sat, 24 Oct 2026 05:09:05 UTC (498 KB)

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