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

arXiv cs.AIOctober 27, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2610.33451v1 Announce Type: new 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, optimizat

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