RSIMed: Scaling Medical Foundation Models beyond Limited Clinical Data via Retrieval-Grounded Self-Evolution

Recursive Self-Improvement (RSI) has gained wide attention for its ability to work with limited data. In a proposer-solver loop, a proposer writes training tasks and a solver learns to solve them through reinforcement learning. Such loops succeed in mathematics and coding, where a program checks every answer. Medicine, however, has scarce, private records and no such verifier, so a wrong model-written reference answer can corrupt training. To solve this problem, we introduce RSIMed, Recursive Self-Improvement in Medicine, one of the first self-evolving pipelines where the proposer co-evolves with the solver in context, on free-text diagnosis from real clinical records. The proposer, a frozen copy of the solver, writes every task from a retrieved medical database and adjusts its difficulty and topics to the solver's live accuracy and past tasks. On rare-disease diagnosis from MIMIC-IV, the accuracy of RSIMed-27B grows linearly with training compute, while a static pool of tasks written once collapses. Without any external model, RSIMed-27B and RSIMed-9B reach 0.516 and 0.471 accuracy, above every GPT-5.6 configuration, whose best reaches 0.399 at 38 times the inference cost of RSIMed-27B. In particular, we see that a stronger teacher does not always produce a stronger student. We further show that the learned skills generalize, as RSIMed-RL-27B raises its adjusted score on out-of-distribution HealthBench Professional from 0.291 to 0.303.