LLM model collapse and human iterated learning share a substrate-independent mechanism but not a shared timescale — transmission-bottleneck width sets the pace

Guo, Wu & Yiu (2026) argue that self-training in large language models and human iterated-learning chains instantiate the same mechanism — a transmission bottleneck whose fixed point is the learners' inductive bias — but the mechanism does not run at the same speed in both substrates. In their LLM experiments, the self-training pipeline used a "50,000-passage bottleneck [that] is wider than typical human experiments … slowing but not eliminating bias amplification." A wider bottleneck lets more of the original distribution's signal survive each generation, so prior-amplification still occurs but takes more generations to dominate than it does in the tight bottlenecks of laboratory iterated-learning experiments (compare the "few generations" convergence in claim-kalish-2007-human-iterated-learning-converges-few-generations).

The consequence is a specific corrective to a natural but wrong intuition: because the mechanism is substrate-independent, it is tempting to expect a single shared "half-life" for how many retransmissions it takes content to decay toward the prior, comparable across Bartlett-style human retelling chains and LLM self-training runs. That expectation fails — bottleneck width, not substrate, is the free parameter that sets the pace. This refines claim-iterated-learning-theory-reframes-model-collapse-as-cultural-evolution: the reframing explains that the two phenomena share a mechanism, but the rate at which each converges must be measured per-bottleneck, not assumed transferable.