LinkedIn and university collaborators find that 7B-to-72B students trained on rejects from smaller frozen models do better on code and math while using less compute
Holding prompts, chosen responses, SeqKD initialization, DPO objective, and training budget fixed while varying only the reject source, the study finds that across Qwen2.5 students from 7B to 72B, rejects generated by smaller frozen Base models with less inference compute train stronger students than the students' own rejects on code generation and mathematical reasoning, and it derives a finite-horizon utility bound in a linearized feature model that motivates three interventions: source mixing, prompt reassignment with lexical permutation, and reference-likelihood reselection.