Self-Play Only Evolves When Self-Synthetic Pipeline Ensures Learnable Information Gain

ArXi:2603.02218v2 Announce Type: replace Large language models (LLMs) make it plausible to build systems that improve through self-evolving loops, but many existing proposals are better understood as self-play and often plateau quickly. A central failure mode is that the loop synthesises data without increasing learnable information for the next iteration. Through experiments on a self-play coding task, we reveal that sustainable self-evolution requires a self-synthesised data pipeline with learnable information that increases across iterations.