Manifest reference

Exhaustive reference for every training-job request and config field. CLI flags map onto these one-for-one — the CLI surface lives on the auto-generated dn train page.

Request wrapper

Every hosted training request carries the same base fields:

Field	Type	Default	Notes
name	str | None	null	Optional job display name.
model	str	—	Required. Base model or adapter target.
project_ref	str | None	null	Workspace project key. Defaults to the workspace default.
run_ref	str | None	null	Optional run association for lineage.
capability_ref	CapabilityRef	—	Required. Versioned capability snapshot to train against.
tags	list[str]	[]	Optional tag list.
backend	literal	—	tinker or ray — set by the request class.
trainer_type	literal	—	sft or rl — set by the request class.
config	trainer config	—	Required. Trainer-specific config object (tables below).

CapabilityRef, DatasetRef, RewardRecipe, and WorldRewardPolicy are all { name, params? } / { name, version } shapes:

Model	Fields
CapabilityRef	name: str, version: str
DatasetRef	name: str, version: str
RewardRecipe	name: str, params: dict (default {})
WorldRewardPolicy	name: str, params: dict (default {}) — see reward recipes for preset names and component composition.

CreateTinkerSFTJobRequest

Hosted SFT on the Tinker backend. backend is "tinker", trainer_type is "sft", config is TinkerSFTJobConfig (below).

TinkerSFTJobConfig

Field	Type	Default	Constraint	Notes
dataset_ref	DatasetRef | None	null	—	Supervised training dataset.
trajectory_dataset_refs	list[DatasetRef]	[]	—	Worlds trajectory datasets. Repeatable.
eval_dataset_ref	DatasetRef | None	null	—	Optional eval corpus; enables post-training eval loss.
max_sequence_length	int | None	null	>= 1	Tokenization cap per example.
batch_size	int | None	null	>= 1	Per-step batch size.
gradient_accumulation_steps	int | None	null	>= 1	Optimizer accumulation steps.
learning_rate	float | None	null	> 0	Optimizer learning rate.
steps	int | None	null	>= 1	Maximum optimizer steps.
epochs	int | None	null	>= 1	Maximum passes over the training set.
lora_rank	int | None	null	>= 1	LoRA rank override.
lora_alpha	int | None	null	>= 1	LoRA alpha override.
checkpoint_interval	int | None	null	>= 1	Checkpoint every N optimizer steps.

Validation (at submit time):

• At least one source is required: dataset_ref, one or more trajectory_dataset_refs, or both. The trainer ETL-merges the inputs when both are set.

CreateTinkerRLJobRequest

Hosted RL on the Tinker backend. backend is "tinker", trainer_type is "rl", config is TinkerRLJobConfig (below).

TinkerRLJobConfig

Field	Type	Default	Constraint	Notes
algorithm	"importance_sampling" | "ppo"	—	—	Required.
task_ref	str | None	null	—	name for latest or name@version for a pinned version.
world_manifest_id	str | None	null	—	Worlds manifest for live rollouts.
world_runtime_id	str | None	null	—	Runtime whose capability binding provides the rollout agent.
world_agent_name	str | None	null	—	Agent selection inside the runtime-bound capability.
world_goal	str | None	null	—	Goal prompt override for live rollouts.
prompt_dataset_ref	DatasetRef | None	null	—	Prompt dataset for verifier-driven RL.
trajectory_dataset_refs	list[DatasetRef]	[]	—	Worlds trajectory datasets for offline RL.
reward_recipe	RewardRecipe | None	null	—	Server-side completion reward. See reward recipes.
world_reward	WorldRewardPolicy | None	null	—	SDK-side trajectory shaping for live Worlds rollouts.
execution_mode	"sync" | "one_step_off_async" | "fully_async"	sync	—	Rollout-group scheduler mode.
prompt_split	str | None	null	—	Dataset split used for prompt sampling.
steps	int | None	null	>= 1	Number of optimizer steps.
lora_rank	int | None	null	>= 1	LoRA rank override.
max_turns	int | None	null	>= 1	Maximum agent turns per episode.
max_episode_steps	int | None	null	>= 1	Maximum environment steps per episode.
num_rollouts	int | None	null	>= 1	Rollouts per training window.
batch_size	int | None	null	>= 1	Training batch size.
learning_rate	float | None	null	> 0	Optimizer learning rate.
weight_sync_interval	int | None	null	>= 1	Sampler weight sync, in optimizer steps.
max_steps_off_policy	int | None	null	>= 1	Rollout staleness budget for async modes.
max_new_tokens	int | None	null	>= 1	Per-completion sampling cap.
temperature	float | None	null	>= 0	Sampling temperature.
stop	list[str] | None	null	—	Stop sequences.
checkpoint_interval	int | None	null	>= 1	Checkpoint every N optimizer steps.

Validation (at submit time):

• At least one input required: prompt_dataset_ref, world_manifest_id, or one or more trajectory_dataset_refs.
• world_runtime_id requires world_manifest_id.
• world_agent_name requires world_runtime_id.
• execution_mode != "sync" requires max_steps_off_policy.
• execution_mode == "one_step_off_async" forces max_steps_off_policy == 1.

CreateRayGRPOJobRequest

Ray-backed GRPO. backend is "ray", trainer_type is "rl", config is RayGRPOJobConfig.

Caution

The Ray GRPO backend is not wired yet — the request validates and queues, but the worker raises NotImplementedError on execution and the job settles to failed. The request shape is documented here for completeness; don’t rely on it in production code.

RayGRPOJobConfig

Field	Type	Default	Constraint	Notes
algorithm	"grpo"	"grpo"	—	Only GRPO is modelled on this config.
task_ref	str	—	—	Required.
prompt_dataset_ref	DatasetRef	—	—	Required.
reward_recipe	RewardRecipe | None	null	—	See reward recipes.
execution_mode	"async" | "colocated" | "distributed"	async	—	Ray scheduling mode.
max_turns	int | None	null	>= 1	Maximum agent turns per episode.
max_episode_steps	int | None	null	>= 1	Environment-step cap per episode.
num_rollouts	int | None	null	>= 1	Rollouts per training window.
batch_size	int | None	null	>= 1	Training batch size.
learning_rate	float | None	null	> 0	Optimizer learning rate.
num_rollout_workers	int | None	null	>= 1	Ray rollout workers.
buffer_size	int | None	null	>= 1	Experience-buffer capacity.
checkpoint_interval	int | None	null	>= 1	Checkpoint every N learner steps.

Job response shape

TrainingJobResponse is the wire shape returned by every hosted-training endpoint. The SDK exposes the same fields under the type name TrainingJob.

Field	Type	Notes
id	str	Training-job identifier.
organization_id	str	Owning organization.
workspace_id	str	Owning workspace.
status	"pending" | "queued" | "running" | "completed" | "failed" | "cancelled"	Current lifecycle state.
name	str | null	Optional display name from the create request.
backend	"tinker" | "ray"
trainer_type	"sft" | "rl"
algorithm	"grpo" | "importance_sampling" | "ppo" | null	Set on RL jobs; null for SFT.
model	str	Base model identifier.
capability	TrainingCapabilitySnapshot	Resolved capability snapshot — name, version, runtime digest.
metrics	dict[str, Any]	Scalar + series metrics. See outputs.
artifacts	dict[str, Any]	Artifact references. See outputs.
tags	list[str]	Tags carried from the create request.
error	str | null	Top-level error string when the job settled to failed.
created_at	str	ISO-8601 submission time.
started_at	str | null	ISO-8601 worker start time.
completed_at	str | null	ISO-8601 terminal-state time.
cancel_requested_at	str | null	ISO-8601. Set when a running job is asked to stop.

Plus the resolved refs from the create request: dataset_ref, trajectory_dataset_refs, task_ref, world_manifest_id, world_runtime_id, world_agent_name, world_goal, prompt_dataset_ref, project_ref, run_ref.

Log entry shape

TrainingJobLogEntry:

Field	Type	Notes
timestamp	str	ISO-8601.
level	"debug" | "info" | "warning" | "error"
message	str	Human-readable line.
data	dict[str, Any]	Optional structured payload.