SageMaker HyperPod での Nova 2.0 の強化ファインチューニング (RFT)

# Note:
# This recipe can run on p5.48xlarge, p5e.48xlarge, and p5en.48xlarge instance types.
run:
  name: "my-rft-run"                           # Unique run name (appears in logs and artifacts).
  model_type: amazon.nova-2-lite-v1:0:256k
  model_name_or_path: nova-lite-2/prod
  data_s3_path: s3://<bucket>/<data-file>      # Training dataset in JSONL format.
  replicas: 4                                   # Number of total training instances.
  generation_replicas: 2                        # Number of total instances dedicated to response generation.
  reward_lambda_arn: arn:aws:lambda:<region>:<account-id>:function:<function-name>

  ## MLFlow configs
  mlflow_tracking_uri: "" # Required for MLFlow
  mlflow_experiment_name: "my-rft-experiment" # Optional for MLFlow. Note: leave this field non-empty
  mlflow_run_name: "my-rft-run" # Optional for MLFlow. Note: leave this field non-empty

## SMHP RFT training configs
training_config:
  max_length: 8192                              # Context window (tokens) for inputs and prompt.
  global_batch_size: 32                         # Total samples per optimizer step across all replicas (16/32/64/128/256).
  reasoning_effort: high                        # Reasoning mode: high, low, or null for non-reasoning.

  data:
    shuffle: true                               # Shuffle training data each epoch.

  rollout:                                      # Controls how responses are generated for advantage calculation.
    rollout_strategy:
      type: off_policy_async                    # Asynchronous rollout for higher throughput.
      age_tolerance: 2                          # Maximum policy age before regeneration.
    advantage_strategy:
      number_generation: 4                      # Samples per prompt to estimate advantages (higher = lower variance but higher cost).
    generator:
      max_new_tokens: 6000                      # Cap on tokens generated per sample.
      set_random_seed: true                     # Seed generation for reproducibility across runs.
      temperature: 1                            # Softmax temperature for sampling.
      top_k: 1                                  # Sample only from top-K logits.
    rewards:
      preset_reward_function: null              # Preset reward functions: exact_match or null for custom.
      api_endpoint:
        lambda_arn: arn:aws:lambda:<region>:<account-id>:function:<function-name>
        lambda_concurrency_limit: 12             # Max concurrent Lambda invocations (throughput vs. throttling).
        lambda_batch_size: 128                  # Number of samples per Lambda invocation.

  trainer:
    max_steps: 2                                # Steps to train for. One step = global_batch_size samples.
    save_steps: 5                               # Save a checkpoint every N steps.
    test_steps: 1                               # Run validation every N reference model updates.
    refit_freq: 4                               # Frequency of reference model updates.
    clip_ratio_high: 0.2                        # PPO clip ratio for policy updates.
    loss_scale: 1.0                             # Scaling factor for the policy loss.

    # RL parameters
    ent_coeff: 0.0                              # Entropy bonus added to the policy loss (higher = more exploration).
    kl_loss_coef: 0.0                           # Weight on the KL penalty between the current and reference policy.

    optim_config:                               # Optimizer settings.
        lr: 1e-6                                # Learning rate.
        weight_decay: 0.0                       # L2 regularization strength (0.0 to 1.0).
        adam_beta1: 0.9
        adam_beta2: 0.95

    peft:                                       # Parameter-efficient fine-tuning (LoRA).
        peft_scheme: "lora"                     # Enable LoRA for PEFT.
        lora_tuning:
            alpha: 64                           # LoRA scaling factor.
            lora_plus_lr_ratio: 64.0            # LoRA+ learning rate scaling factor (0.0 to 100.0).