Recursive Self-Improvement

Enable a model to generate its own training data, filter it, and retrain on it in iterative cycles, bootstrapping capability without external supervision. In recursive self-improvement, the model generates candidate solutions or reasoning traces, filters them using a reward signal or self-consistency check, and retrains on the filtered outputs. Each iteration produces a slightly better model, which generates better data for the next round. Key variants include STaR (bootstrapped reasoning), Self-Rewarding (model judges its own outputs), and ReST (iterative self-training with rejection sampling).