Bayesian Fine‑Tuning Unlocks LLM Reasoning

Training language models by compressing symbolic Bayesian reasoning demonstrations into neural weights can produce general probabilistic reasoning that transfers across domains, not just task‑specific pattern matching. In practice, models trained on synthetic Bayesian tasks generalized to unrelated real‑world applications, implying the training signal (how you teach reasoning) matters as much as model size. This suggests a route to robust, domain‑general LLM reasoning without only relying on scaling context windows. — If correct, this changes capability projections and governance needs because relatively modest technique changes (training signals) could unlock broad, transferable reasoning in LLMs faster than size‑only forecasts expect.