/**
 * Export PRM-graded traces as training data for downstream reward-model
 * fine-tuning. Canonical format is NDJSON of
 * `{ trajectory_text, step_index, rubric, score }` so a small model can
 * learn to predict step rewards from step context.
 *
 * The framework doesn't train the model — we emit the data; callers
 * plug it into their preferred trainer (TRL, Unsloth, custom).
 */
import type { Span } from '../trace/schema';
import type { TraceStore } from '../trace/store';
import type { PrmGradedTrace } from './rubric';
export interface PrmTrainingSample {
    runId: string;
    spanId: string;
    rubricId: string;
    score: number;
    /** Serialized step context — step + surrounding conversation. */
    context: {
        priorTurns: Array<{
            role: string;
            content: string;
        }>;
        step: {
            kind: Span['kind'];
            text: string;
        };
    };
    /** Optional evidence + rationale for auditability. */
    rationale?: string;
    evidence?: string;
}
export declare function exportTrainingData(store: TraceStore, graded: PrmGradedTrace[], options?: {
    contextWindow?: number;
}): Promise<PrmTrainingSample[]>;
/** NDJSON serialization — write to file or stream directly to a trainer. */
export declare function toNdjson(samples: PrmTrainingSample[]): string;
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