Parameters

Note

Your parameters in your configuration file must match the keywords in __init__ of your model, optimizer, learning rate scheduler, or loss criterion. For example, if you want to set base_lr in the learning scheduler CyclicLR, you will need:

[parameters]
[parameters.learning_rate]
base_lr = 0.01

model

All the parameter settings that need to be passed as keyword parameters when initializing the model, such as num_classes or cut_layer. The set of parameters permitted or needed depends on the model.

optimizer

All the parameter settings that need to be passed as keyword parameters when initializing the optimizer, such as lr, momentum, or weight_decay. The set of parameters permitted or needed depends on the optimizer.

learning_rate

All the parameter settings that need to be passed as keyword parameters when initializing the learning rate scheduler, such as gamma. The set of parameters permitted or needed depends on the learning rate scheduler.

loss_criterion

All the parameter settings that need to be passed as keyword parameters when initializing the loss criterion, such as size_average. The set of parameters permitted or needed depends on the loss criterion.

SmolVLA + LeRobot parameter contract

Config() keeps nested keys under [parameters] as dot-accessible nodes. For the SmolVLA + LeRobot integration, define the following sections.

Config key	Purpose	Consumption path
data.datasource = "LeRobot"	Selects the robotics datasource family.	plato.datasources.registry.get() chooses the datasource module.
trainer.type = "lerobot"	Selects the robotics trainer backend.	plato.trainers.registry.get() chooses the trainer class.
trainer.model_type = "smolvla"	Selects the model family.	plato.models.registry.get() resolves the model factory.
trainer.model_name = "smolvla"	Selects the concrete model entry point.	plato.models.registry.get() resolves the model name.
parameters.policy.type	Policy family identifier (smolvla in v1).	Consumed by plato/models/smolvla.py and plato/trainers/lerobot.py via Config().parameters.policy.
parameters.policy.path	Pretrained policy source (Hub/local path).	Consumed by plato/models/smolvla.py via Config().parameters.policy.path.
parameters.policy.finetune_mode	Full fine-tune vs adapter mode switch.	Consumed by plato/trainers/lerobot.py to decide trainable params.
parameters.policy.precision	Runtime precision flag (fp32/fp16/bf16).	Consumed by plato/trainers/lerobot.py for dtype/autocast setup.
parameters.policy.device	Runtime device flag (cpu/cuda/mps).	Consumed by plato/trainers/lerobot.py for device placement.
parameters.dataset.repo_id	LeRobot dataset identifier.	Consumed by plato/datasources/lerobot.py dataset loader.
parameters.dataset.delta_timestamps	Temporal window selection per modality key.	Consumed by plato/datasources/lerobot.py sampling/windowing logic.
parameters.transforms.*	Image transform controls (image_size, normalize, interpolation/crop options).	Consumed by plato/datasources/lerobot.py preprocessing pipeline.

Constructor-ready dictionaries

SmolVLA/LeRobot components should convert section nodes to plain dictionaries when passing keyword arguments into constructors:

from plato.config import Config

cfg = Config()
policy_kwargs = cfg.parameters.policy._asdict()
dataset_kwargs = cfg.parameters.dataset._asdict()
transform_kwargs = cfg.parameters.transforms._asdict()

Example

[data]
datasource = "LeRobot"

[trainer]
type = "lerobot"
model_type = "smolvla"
model_name = "smolvla"

[parameters.policy]
type = "smolvla"
path = "lerobot/smolvla_base"
finetune_mode = "full"
precision = "bf16"
device = "cuda"

[parameters.dataset]
repo_id = "lerobot/pusht_image"
delta_timestamps = { observation_image = [-0.2, -0.1, 0.0] }

[parameters.transforms]
image_size = [224, 224]
normalize = true
interpolation = "bilinear"