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32030e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | """Rewards for RL fine-tuning (single-turn families): the benchmark score of a completion.
from ejbench_eval.data import load_tasks
from ejbench_eval.reward import TaskRewarder
reward = TaskRewarder(load_tasks("path/to/dataset", "train"))
# TRL GRPOTrainer: reward_funcs=[reward]; the dataset needs a ``task_id`` column.
Train on ``train`` only. Never compute rewards on ``test`` or ``test_unseen_cell``.
"""
from __future__ import annotations
from typing import Any, Iterable
from .parsing import extract_json
from .scoring import score_task
def completion_text(completion: Any) -> str:
"""TRL passes strings, or lists of chat messages for conversational datasets."""
if isinstance(completion, str):
return completion
if isinstance(completion, list) and completion and isinstance(completion[-1], dict):
return str(completion[-1].get("content", ""))
return str(completion)
def reward_from_text(task: dict, text: str) -> float:
answer = extract_json(text)
if answer is None:
return 0.0
try:
return float(score_task(task, answer)["score"])
except Exception:
return 0.0
class TaskRewarder:
__name__ = "ejbench_score"
def __init__(self, tasks: Iterable[dict]):
self.tasks = {task["task_id"]: task for task in tasks if task["interaction"] == "single_turn"}
bad = [task_id for task_id, task in self.tasks.items() if task.get("split") != "train"]
if bad:
raise ValueError(f"Rewards are for train tasks only; got non-train tasks such as {bad[:3]}")
def __call__(self, completions: list, task_id: list[str], **_: Any) -> list[float]:
return [reward_from_text(self.tasks[tid], completion_text(c)) for c, tid in zip(completions, task_id)]
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