Results for "gradual scaling"

Scaling law optimizing compute vs data.

Increasing model capacity via compute.

Increasing performance via more data.

Empirical laws linking model size, data, compute to performance.

Generative model that learns to reverse a gradual noise process.

Dynamic resource allocation.

The relationship between inputs and outputs changes over time, requiring monitoring and model updates.

Adjusting learning rate over training to improve convergence.

Incremental capability growth.

The degree to which predicted probabilities match true frequencies (e.g., 0.8 means ~80% correct).

Techniques that stabilize and speed training by normalizing activations; LayerNorm is common in Transformers.

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

Stochastic generation strategies that trade determinism for diversity; key knobs include temperature and nucleus sampling.

Scales logits before sampling; higher increases randomness/diversity, lower increases determinism.

Capabilities that appear only beyond certain model sizes.

Cost to run models in production.

Cost of model training.

Probabilities do not reflect true correctness.

Methods like Adam adjusting learning rates dynamically.