feat: Add validation loss tracking, early stopping, and checkpoint cleanup

src/lerobot/configs/train.py

@@ -33,6 +33,24 @@
 TRAIN_CONFIG_NAME = "train_config.json"
 
 
+@dataclass
+class EarlyStoppingConfig:
+    """Configuration for early stopping based on validation/eval loss."""
+
+    # Enable early stopping
+    enable: bool = False
+    # Stop training if no improvement for this many steps
+    patience_steps: int = 10000
+    # Minimum change to qualify as an improvement (relative)
+    min_delta: float = 0.0
+    # Metric to monitor: "val_loss" for validation dataset loss, "eval_success" for sim env success rate
+    monitor: str = "val_loss"
+    # Whether higher is better (True for success rate, False for loss)
+    higher_is_better: bool = False
+    # Whether to shuffle episodes before splitting into train and validation sets
+    shuffle_episodes: bool = True
+
+
 @dataclass
 class TrainPipelineConfig(HubMixin):
     dataset: DatasetConfig
@@ -60,11 +78,17 @@ class TrainPipelineConfig(HubMixin):
     save_checkpoint: bool = True
     # Checkpoint is saved every `save_freq` training iterations and after the last training step.
     save_freq: int = 20_000
+    # Fraction of episodes to use for validation (0.0 = no validation, e.g. 0.1 = 10% for validation).
+    # Validation loss is computed at each eval_freq step using inference (select_action).
+    validation_fraction: float = 0.0
     use_policy_training_preset: bool = True
     optimizer: OptimizerConfig | None = None
     scheduler: LRSchedulerConfig | None = None
     eval: EvalConfig = field(default_factory=EvalConfig)
     wandb: WandBConfig = field(default_factory=WandBConfig)
+    early_stopping: EarlyStoppingConfig = field(default_factory=EarlyStoppingConfig)
+    # Maximum number of checkpoints to keep (0 = keep all)
+    keep_last_n_checkpoints: int = 0
     peft: PeftConfig | None = None
 
     # RA-BC (Reward-Aligned Behavior Cloning) parameters