Momentum

Popular optimizer combining momentum and per-parameter adaptive step sizes via first/second moment estimates.

Reinforcement learning from human feedback: uses preference data to train a reward model and optimize the policy.

A preference-based training method optimizing policies directly from pairwise comparisons without explicit RL loops.

Minimizing average loss on training data; can overfit when data is limited or biased.

Penalizes confident wrong predictions heavily; standard for classification and language modeling.

Average of squared residuals; common regression objective.

Iterative method that updates parameters in the direction of negative gradient to minimize loss.