Post #8019

A recent arXiv paper – “On the Limits of Self-Improving in Large Language Models” – doesn’t just argue against RSI. It formally proves it’s self-defeating. The core idea: model the self-referential training loop as a dynamical system on the space of probability distributions. When a model trains on its own generated data (synthetic outputs), it’s not learning from reality anymore – it’s learning from a distorted reflection of itself. The paper proves that under a diminishing supply of fresh, authentic data, this system converges to a fixed point – a degenerate distribution with low diversity and high bias. The technical term is model collapse, and it’s been observed empirically too. But now there’s a formal proof that it’s inevitable, not just a bad luck outcome. In plain terms: the model doesn’t climb toward superintelligence. It slowly forgets what the real world looks like. The proof also extends beyond single LLMs – it covers ecosystems of interacting models and multi-modal systems. So no, a committee of AIs feeding each other outputs doesn’t escape the problem. It might actually make it worse. devsimsek's blog link to original paper