docs(blog): explain continual fine-tuning acceptance with a synthetic example

[LauraGPT]

Summary

Add one bilingual applied explanation, Did fine-tuning actually make your model better?, to the technical-explanations archive. It uses the real evaluation-policy lesson from #3388 and the released checkpoint-ranking fix in #3677, without closing either discussion or claiming the user's forgetting problem is solved.

• Separate reference validation, constrained checkpoint selection and independent service acceptance.
• Clearly label the numeric example and reproducible PNG as synthetic, not measured SenseVoice results or a recommended regression threshold.
• Link the maintained v1.4.15 fine-tuning recipe instead of duplicating installation commands.
• Preserve the one-lead/four-selected homepage. Keep the comparison table readable at 320/390/1440px in both languages.

Verification

• Final source: 20 article and legacy tests passed; 14 editorial/browser tests passed, with 12 article screenshots inspected.
• Mobile table regression: before the scoped fix, all four EN/ZH 320/390px cases failed and both desktop cases passed. Final six article cases pass.
• Two final builds are identical across 270 files; each validates 204 HTML pages.
• The complete 487-test product-site suite passed on the final source in 83 seconds using eight pytest-xdist workers on ind-gpu8. Earlier serial 480-second timeouts are retained as failed attempts, not counted as passing. CI remains unchanged and its exact-head full Product site checks are required before merge.
• Content was independently reviewed against the public issue and v1.4.15 source. No model training, benchmark, hosted inference, trainer change or PyPI release is claimed.

Source and publication backups are retained outside the repository. Prepared with Codex assistance. No issue-closing keyword is used intentionally.