Audit Before You Distill: A Controlled Workflow for Reasoning Transfer into Compact Medical Language Models
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arXiv:2609.24799v1 Announce Type: new Abstract: Post-training quantization (PTQ) enables efficient deployment of large language models, and PTQ methods are usually optimized and evaluated with generi...
arXiv:2610.17690v1 Announce Type: new Abstract: Medical multimodal reasoning models are typically trained on teacher-generated rationales, discarding the valid reasoning in students' own failed traje...
Medical multimodal reasoning models are typically trained on teacher-generated rationales, discarding the valid reasoning in students' own failed trajectories, while general-domain correction methods...
The paper investigates how much training data is required for on‑policy distillation (OPD) of foundation models. It shows that in two single‑teacher settings, only eight real prompts can match the performance of 17,000 problems, and that 64 self‑generated questions can replace full external datasets. In multi‑teacher scenarios, 1,000 generated questions achieve comparable results to 7,000 real examples, while fully implicit question generation works only in limited cases.
arXiv:2609.09684v1 Announce Type: new Abstract: Medical question-answering datasets often contain answer labels, whereas high-quality rationales remain scarce, noisy, or costly to validate. This chan...
arXiv:2610.27450v1 Announce Type: new Abstract: On-policy distillation (OPD) can succeed with surprisingly few training questions, motivating a view of questions as carriers of distributional knowled...