Solves the 'recursive drift' problem in self-improving LLMs by using symbolic verification to gate training data quality.
March 24, 2026
Original Paper
Stabilizing Iterative Self-Training with Verified Reasoning via Symbolic Recursive Self-Alignment
arXiv · 2603.21558
The Takeaway
By moving beyond simple outcome-based filtering to step-level symbolic verification (e.g., using SymPy), it prevents models from learning from 'lucky guesses' with flawed reasoning. This enables sustained capability growth across multiple iterations of self-training.
From the abstract
Recursive self-improvement--where a model iteratively trains on its own outputs--promises sustained capability growth but faces a fundamental obstacle: recursive drift. As models train on self-generated data across multiple iterations, errors in intermediate reasoning compound, leading to mode collapse and performance degradation. We propose Neuro-Symbolic Recursive Self-Alignment (NSRSA), which stabilizes iterative self-training by embedding a symbolic verification subsystem that gates training