Beyond Temperature: Hyperfitting as a Late-Stage Geometric Expansion
Recent work has identified a counterintuitive phenomenon termed "Hyperfitting", where fine-tuning Large Language Models (LLMs) to near-zero training loss on small datasets surprisingly enhances open-ended generation quality and mitigates repetition in greedy decoding. While effective, the underlying mechanism remains poorly understood, with the extremely low-entropy output distributions suggesting a potential equivalence to simple temperature scaling. In this work, we demonstrate that this phenomenon is fundamentally distinct from distribution sharpening; entropy-matched control experiments reveal that temperature scaling fails to replicate the diversity gains of hyperfitting. Furthermore...
| Authors | Meimingwei Li, Yuanhao Ding, Esteban Garces Arias et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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