src/explicit_learning/training/__init__.py

"""CPU-testable contracts shared by SFT, RL, and evaluation adapters.

The modules in this package deliberately keep GPU frameworks behind lazy
launcher checks.  Dataset admission, target construction, reward parsing,
fairness scheduling, and token accounting can therefore be tested before a
training server is allocated.
"""

from .answers import (
    UNANSWERABLE_TOKEN,
    AnswerParse,
    NormalizationError,
    answers_equal,
    normalize_answer,
    parse_answer,
)
from .data import AdmissionError, AdmissionRequest, AdmittedGroup, admit_group, load_groups
from .ledger import CompletionTokenLedger, LedgerError
from .rewards import (
    ALL_ARMS,
    LEGACY_ARM_ALIASES,
    ArmPlan,
    RewardTrace,
    RewardWeights,
    canonical_arm,
    plan_all_arms,
    plan_four_arms,
    score_completion,
)
from .slots import (
    ComparisonSlot,
    build_comparison_slots,
    comparison_slot_from_row,
    comparison_slot_manifest_sha256,
)
from .targets import TargetMappingError, defacto_target, ours_target

__all__ = [
    "ALL_ARMS",
    "LEGACY_ARM_ALIASES",
    "UNANSWERABLE_TOKEN",
    "AdmissionError",
    "AdmissionRequest",
    "AdmittedGroup",
    "AnswerParse",
    "ArmPlan",
    "ComparisonSlot",
    "CompletionTokenLedger",
    "LedgerError",
    "NormalizationError",
    "RewardTrace",
    "RewardWeights",
    "TargetMappingError",
    "admit_group",
    "answers_equal",
    "build_comparison_slots",
    "canonical_arm",
    "comparison_slot_from_row",
    "comparison_slot_manifest_sha256",
    "defacto_target",
    "load_groups",
    "normalize_answer",
    "ours_target",
    "parse_answer",
    "plan_all_arms",
    "plan_four_arms",
    "score_completion",
]