MedLLM: An Open Medical Language Model at the Sub-Billion Scale
Open medical language models have converged on a single scale: every widely used system runs at 7B parameters or more, leaving the sub-billion regime uncharacterized. We present MedLLM, an open 0.1B-parameter medical language model trained through a fully open three-phase pipeline: general pretraining with curriculum sequence-length scheduling, domain fine-tuning on MedFineWeb, a reference-guided medical corpus we release that is selected from general web data by embedding similarity to medical question-answering (QA) data, and preference-aligned fine-tuning combining SFT with direct preference optimization (DPO). Across medical benchmarks, MedLLM shows a pattern visible only at sub-billi...
| Authors | Maxx Richard Rahman, Asim Ahmed, Mihan Mohagheghzadeh et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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