A recent research paper on adapter‑based fine‑tuning finds that the most noticeable impact of fine‑tuning large language models is a change in surface form rather than a genuine boost in capability. The authors report that “fine‑tuning shifts output…
Continue reading Fine‑tuning Shifts Model Output Form Before True Competence, New Study ShowsA new research paper reveals that the most noticeable impact of fine‑tuning large language models is a shift in output style rather than a true change in capability. Experiments showed that fine‑tuned models often register lower scores on…
Continue reading Fine‑tuning Mostly Alters Style, Not Competence, Recent Study FindsA recent paper from Beijing Institute of Technology and Xiaohongshu introduces “amortized agentic policy discovery,” a technique that trains a policy to choose decoding strategies based on each user’s preferences instead of seeking a single, shared optimal point.…
Continue reading New Research Shows Personalized Test‑Time Scaling Lets LLMs Adapt to Users Without Fine‑Tuning
