import { type TasksetPackage } from "./taskset-package-contracts.js"; import { buildTasksetTrainingBundle } from "./training-bundle.js"; import { type ManagedTrainingPreparationInput } from "./managed-training-preparation.js"; import { type TrainingCapabilities } from "./training.js"; export type OrdinaryTasksetQualificationInput = Omit[0], "taskset" | "executionMode" | "qualificationSource" | "tasksetRelease" | "rewardExecution" | "verifierAssets" | "tasksetAssetBytes" | "evaluationSource" | "approval"> & { package: TasksetPackage; qualificationTaskId: string; approval: ManagedTrainingPreparationInput["approval"] & { approvedAt: string; }; idempotencyKey?: string; runtimeCapabilities?: TrainingCapabilities; }; /** Prepare a no-update original-learner run from the exact published ordinary * package, with one explicit source task selected for its rollout group. * Splits, families, task IDs and private grader bytes are preserved; * this function does not create train/held-out data or approve any source. */ export declare function prepareOrdinaryTasksetQualification(input: OrdinaryTasksetQualificationInput): Promise<{ artifact: { schemaVersion: "openpond.trainingInputArtifactUpload.v2"; kind: "portable_training_bundle" | "reward_model_dataset"; idempotencyKey: string; sourceManifest: { id: string; contentHash: string; }; payload: unknown; contentHash: string; }; submission: { schemaVersion: "openpond.trainingJobSubmission.v2"; idempotencyKey: string; name: string; source: { origin?: { schemaVersion: "openpond.trainingHandoffOrigin.v1"; teamId: string; projectId: string | null; configurationId: string; source: { executionId: string; passId: string | null; manifestHash: string; resultHash: string; }; dataset: { id: string; revision: number; contentHash: string; }; target: { kind: "model"; modelId: string; configurationHash: string; } | { kind: "harness"; source: { profileId: string; sourceRevision: string; harnessRelease: { id: string; contentHash: string; }; catalogHash: string; definitionId: string; definitionHash: string; target: { kind: "profile"; } | { kind: "workflow"; workflowId: string; } | { kind: "skill"; skillPath: string; } | { kind: "agent_action"; actionId: string; }; environmentHash: string; externalDatasetBinding?: { schemaVersion: "openpond.profileExternalDatasetBinding.v1"; dataset: { id: string; contentHash: string; revision: number; }; packageHash: string; profileId: string; target: { kind: "profile"; } | { kind: "workflow"; workflowId: string; } | { kind: "skill"; skillPath: string; } | { kind: "agent_action"; actionId: string; }; declaredProfileCatalogHash: string | null; protectedProfileClosureHash: string; recordedOrigin?: { execution: { id: string; contentHash: string; }; originalEvidence: { id: string; contentHash: string; }; members: { caseId: string; seed: string; fixtureId: string; sourceId: string; snapshotHash: string; boundaryRevisionHash: string; }[]; } | undefined; gradingSource?: { pass: { id: string; contentHash: string; }; evidence: { id: string; contentHash: string; }; } | undefined; split: "frozen_eval" | "test" | "train" | "validation"; population: { taskId: string; seed: string; fixtureId: string | null; }[]; evaluators: { release: { id: string; contentHash: string; revision: string | number; }; feedbackKey: string; configurationHash: string; output: "boolean" | "category" | "score"; categories: string[]; }[]; fieldMappingsHash: string; fieldMappings: { graderId: string; fields: { destination: "evaluatorContext" | "expectedOutput" | "input" | "output"; path: string[]; source: "evaluatorContext" | "expectedOutput" | "input" | "output"; sourcePath: string[]; }[]; }[]; environmentHash: string; privateDatasetClosureHash: string; criterion: { minimumPassRate: number; requireComplete: boolean; }; contentHash: string; } | undefined; sourceCandidate?: { candidateId: string; frozenRevision: number; frozenHash: string; harnessRelease: { id: string; contentHash: string; }; protectedClosureHash: string; } | undefined; }; model: { modelId: string; configurationHash: string; }; } | { kind: "agent"; source: { harnessRelease: { id: string; contentHash: string; }; agentSnapshot: { id: string; contentHash: string; }; sourcePackageHash: string; }; model: { modelId: string; configurationHash: string; }; } | { kind: "fixture"; configurationHash: string; }; graders: { feedbackKey: string; evaluator: { id: string; contentHash: string; }; expectedCount: number; scoredCount: number; score: number | null; }[]; contentHash: string; } | undefined; modelProject: { id: string; portableProjectId: string; revision: number; contentHash: string; }; harnessRunManifest: { id: string; contentHash: string; }; harnessRelease: { id: string; contentHash: string; }; taskset: { id: string; contentHash: string; revision: number; }; tasksetRelease: { id: string; contentHash: string; }; evaluation?: { taskset: { id: string; contentHash: string; revision: number; }; dataset: { id: string; contentHash: string; }; } | null | undefined; dataset: { id: string; contentHash: string; }; evidenceSets: { id: string; contentHash: string; }[]; }; job: { kind: "reward_model_train"; baseModel: { schemaVersion: "openpond.baseModelPreference.v1"; modelId: string; revision: string | null; tokenizerRevision: string | null; chatTemplateHash: string | null; modelAssetId: string | null; source: "builtin" | "local" | "managed"; }; preferenceDatasetRelease: { id: string; contentHash: string; }; processorRelease: { id: string; contentHash: string; }; recipe: { [x: string]: unknown; schemaVersion: "openpond.rewardModelRecipe.v1"; method: "reward_model"; parameterization: "lora_with_scalar_head"; }; } | { kind: "policy_optimize"; executionMode?: "qualification" | undefined; baseModel: { schemaVersion: "openpond.baseModelPreference.v1"; modelId: string; revision: string | null; tokenizerRevision: string | null; chatTemplateHash: string | null; modelAssetId: string | null; source: "builtin" | "local" | "managed"; }; recipe: { [x: string]: unknown; schemaVersion: string; method: "dpo" | "grpo" | "opd" | "opsd" | "ppo" | "sdft" | "sdpo" | "sft"; parameterization: "full" | "lora"; }; rewardSource: { kind: "deterministic"; grader: { id: string; contentHash: string; }; composer: { id: string; contentHash: string; } | null; } | { kind: "learned_reward"; rewardModelVersion: { id: string; contentHash: string; }; qualificationReport: { id: string; contentHash: string; } | null; evaluationReferences: { id: string; contentHash: string; }[]; scorerArtifact: { artifactRef: string; contentHash: string; executionReceipt: { id: string; contentHash: string; }; }; processorRelease: { id: string; contentHash: string; }; rewardComposerRelease: { id: string; contentHash: string; }; }; resumeFrom: { id: string; contentHash: string; } | null; }; requestedCapabilities: { id: string; version: string | null; required: boolean; }[]; placementObjective: "balanced" | "economical" | "fast"; gpuSelection?: { mode: "automatic"; objective: "balanced" | "economical" | "fast"; gpuCount?: 1 | 2 | undefined; } | { mode: "manual"; availabilityId: string; gpuType: string; gpuCount?: 1 | 2 | undefined; } | undefined; physicalPlacement?: { mode: "reuse_only"; physicalWorkerId: string; providerPodId: string; workerImageDigest: string; baseProfileSha256: string; gpuType: string; sourceJobId: string; sourceInputManifestSha256: string; ownerTeamId: string; deadlineAt: string; } | undefined; budget: { maximumSpendUsd: number; maximumWallSeconds: number; }; approval: { approvalHash: string; approvedAt: string; exportApproved: boolean; maximumSpendUsd: number; retentionDays: number | null; region: string | null; }; contentHash: string; }; bundle: import("./training-bundle.js").TasksetTrainingBundle; }>; //# sourceMappingURL=training-qualification-preparation.d.ts.map