# Core operations The training session APIs expose a set of first-class operations that you compose into a training loop. Each operation is an API call against your active session. The model remains loaded on GPUs between calls, and gradient state persists within a training step. ## forward\_backward + Compute forward and backward passes in a single call. + Accumulate gradients across batches. + Specify your loss function (`cross_entropy`, `importance_sampling`, `ppo`, `cispo`, or a custom loss). ## forward + Run a forward-only pass (no gradient computation). + Compute loss and metrics without updating optimizer state. + Useful for evaluation, importance weighting, and KL divergence calculation. + Same loss function options as `forward_backward`. ## optim\_step + Update weights based on accumulated gradients. + Configure Adam hyperparameters (`learning_rate`, `beta1`, `beta2`, `eps`, `weight_decay`, `gradient_clip_norm`). + Full control over Adam optimization and LoRA\+ learning rate scaling. ## sample + Generate tokens for evaluation, RL rollouts, or inference. + Generate multiple sequences per prompt (`num_samples`). + Optionally return per-token prompt log-probabilities. + Configurable `temperature`, `top_k`, `top_p`, `seed`, `max_tokens`, and stop sequences. ## compute\_logprobs + Score existing token sequences without generating new tokens. + Returns per-token log probabilities over the prompt. + Essential for RL reference policies, reranking, and importance sampling. + Works against the base model or any saved checkpoint. ## save\_weights + Save inference-ready model weights as a model package. + Optionally merge LoRA adapters into full standalone weights. + Output is deployable directly, with no optimizer state overhead. + Distinct from `save_state`: export for serving versus checkpoint for resumption. ## save\_state + Checkpoint full training state (sharded weights and optimizer state). + Resume training from any checkpoint via `load_state`. + Registered as a versioned model package for lifecycle tracking. ## load\_state + Resume training from any previously saved checkpoint. + `load_state` restores weights only; the optimizer starts fresh. + `load_state_with_optimizer` restores weights and optimizer state, so training resumes exactly where the checkpoint left off. + Use it to resume after an interruption or to branch a new experiment from a saved point.