Reconciling Contradictory Views on the Effectiveness of SFT in LLMs: An Interaction Perspective

In three linesarXiv paper on supervised fine-tuning (SFT) effectiveness for LLMs. Authors show SFT primarily removes noise-like token interactions but rarely acquires reliable new ones. The denoising phase is extremely brief; continued fine-tuning introduces overfitted interactions. Implications for early stopping and LLM training.

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