125M legal SLM aligned with RLAIF (RL from AI feedback). A Bradley-Terry reward model was trained on ~681 AI-written preference pairs, then the closed-book QA policy (slm-125m-qa) was improved by best-of-N reward-weighted SFT: sample N answers per prompt, keep the reward-model's top pick, fine-tune on the winners.