arXiv:2605.30524v1 Announce Type: new Abstract: Large language models are now adapted through chains of post-training stages rather than through a single instruction-tuning pass. This paper studies whether such sequential post-training gradually compresses internal representations into low-rank, anisotropic, and homogeneous feature spaces. We define a measurement suite for hidden states, logits, token trajectories, and LoRA updates, and we use it to analyze supervised fine-tuning, preference optimization, safety/refusal tuning, math and code specialization, and long chain-of-thought tuning und
The increasing complexity and sequential nature of large language model training pipelines necessitate a deeper understanding of internal representation dynamics.
Representation collapse indicates a fundamental limitation in current LLM scaling and fine-tuning methods, impacting model performance, efficiency, and generalization.
The research provides a new framework and measurement suite for diagnosing and potentially mitigating suboptimal internal representations in advanced LLMs.
- · AI researchers
- · GPU manufacturers (due to need for more robust architectures)
- · Cloud AI providers (integrating new efficiency methods)
- · LLM developers reliant on simple sequential fine-tuning
- · Companies with less sophisticated AI infrastructure
Ongoing research will focus on developing new fine-tuning algorithms that prevent or mitigate representation collapse to improve LLM capabilities.
Advanced diagnostic tools for LLM internal states will become standard, shifting development practices towards more interpretability and control.
The development of LLMs may become more engineering-driven than purely scaling-driven, with emphasis on architectural and training procedure innovations over raw parameter counts.
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Read at arXiv cs.LG