IMPLEMENTING Iterative Finetuning is Mostly Idempotent”*, arXiv:2605.01130
Here is the plain-text version of the provided content. All code blocks, file tags, and markdown formatting have been removed. The charts are referenced but not included; their key observations and data are fully described in text. USING GEMINI AND DEEPSEEK --- # Plain‑Text Performance Evaluation and Comparison ## 1. Step‑by‑Step Performance Evaluation Protocol To rigorously compare models **without** the paper’s principles versus models **with** the paper’s principles (*“Iterative Finetuning is Mostly Idempotent”*, arXiv:2605.01130), a recursive N‑cycle fine‑tuning pipeline is used. **Pipeline Overview** - Start from a base model checkpoint (M₀). - Two parallel paths are followed for N cycles (N = 0 to 5): **Without Paper Principles (Naive Continual Loop)** - The model’s weights are updated continuously across cycles. - At each cycle i, the current model Mᵢ generates synthetic outputs Dᵢ. - The model is then trained directly on its own generate...