Self‑Training Boosts Ordered Rényi Crossings, Emphasizing Richness Over Evenness
fine-tuning training
| Source: ArXiv | Original article
A new arXiv paper shows recursive self‑training of GPT‑2 over nine generations trims vocabulary and reduces the dominance of frequent function words.
A new arXiv pre‑print titled **“Richness Is Not Evenness: Ordered Renyi Crossings under Self‑Training”** (arXiv:2611.08892v1) reports fresh insights into recursive self‑training of language models. The authors fine‑tuned an English GPT‑2 on text it generated itself for nine successive generations, adjusting the training regime across runs. Their experiments show that self‑training can shrink a model’s active vocabulary while simultaneously diminishing the dominance of high‑frequency function words.
The findings matter because they touch on a long‑standing concern known as *model collapse*, where repeated self‑training leads to degenerated language use and reduced expressive power. By demonstrating that vocabulary richness can be preserved—or even enhanced—without the usual over‑reliance on common tokens, the work suggests a pathway to more balanced language representations. This could improve downstream tasks that depend on nuanced lexical variety, such as low‑resource translation or domain‑specific generation, where over‑emphasis on function words hampers performance.
The study also introduces the concept of “Ordered Rényi Crossings” as a metric for tracking how token distributions evolve under self‑training, offering researchers a new diagnostic tool. Going forward, the community will likely watch for follow‑up experiments that apply the method to larger models or multilingual settings, and for any attempts to integrate the metric into training pipelines to guard against collapse. If the approach scales, it could reshape best practices for self‑supervised fine‑tuning across the rapidly expanding landscape of generative AI.
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