A new paper on arXiv provides a comprehensive framework for understanding and optimizing the entire lifecycle of large model training. It proposes a six-dimensional analytical framework covering data, objective functions, optimization strategies, system constraints, evaluation, and governance. The research systematically reviews 395 core references from 2017 to 2026, identifying key cross-stage effects and deriving three core findings regarding data quality's impact on scaling laws, the evolving boundaries of post-training alignment methods, and the structural challenges of multimodal training. AI
IMPACT Provides a structured approach to understanding and improving LLM and MLLM training, potentially guiding future research and development.
RANK_REASON The cluster contains a research paper detailing a new framework for large model training. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Direct Preference Optimization
- Hugging Face
- large language models
- MLLMs
- Pass@1
- Pass@K
- reinforcement learning from human feedback
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