Why lowering sampling temperature fixes generative models: new study on limited-data bias
bravo_abad · x · 2026-11-08
Fields and colleagues investigate why lowering sampling temperature after training improves generative outputs (e.g., in protein design).
Key findings:
- Root cause: limited training data leaves too much probability mass on unlikely configurations — each is rare, but together they account for a substantial fraction of generations.
- Method: using energy-based models in controlled systems (including a small lattice of interacting spins), they show lowering temperature partially corrects this error, even without regularization during training.
- Limits: the correction breaks down when likely and unlikely configurations have sharply separated boundaries.
The work reframes temperature tuning as an approximate correction for a systematic bias induced by limited training data rather than an ad-hoc trick.
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