Can`t reproduce the score after refitting TPOT regressor

Hi
The exported pipeline of TPOT stating that the 'Average CV score on the training set was: -128.90187963562252' (neg_MAE)
However, refitting the pipeline with the same exact training set yields way too smaller MAE around (35).
Moreover predicting unseen test set would yield an MAE around (140 ) which is in line with what the exported pipeline stating.
I am a bit confused and wondering how to reproduce the error score on the training set.
The pipeline seems to be overfitting right??
here is a piece of code used to get the pipeline


cv = RepeatedKFold(n_splits=4, n_repeats=1, random_state=1)
model = TPOTRegressor(generations=10, population_size=25, offspring_size=None, mutation_rate=0.9,
crossover_rate=0.1, scoring='neg_mean_absolute_error', cv=cv, subsample=0.75,
n_jobs=-1, max_time_mins=None, max_eval_time_mins=5,random_state=42,
config_dict=None, template=None, warm_start=False, memory=None, use_dask=False,
periodic_checkpoint_folder=None, early_stop=3, verbosity=2,
disable_update_check=False, log_file=None
)
model.fit(train_df[x], train_df[y])

The Exported model

# Average CV score on the training set was: -128.90187963562252
exported_pipeline = make_pipeline(
StackingEstimator(estimator=LassoLarsCV(normalize=True)),
StackingEstimator(estimator=ExtraTreesRegressor(bootstrap=True, max_features=0.4, min_samples_leaf=1,
min_samples_split=7, n_estimators=100)),
PolynomialFeatures(degree=2, include_bias=False, interaction_only=False),
ExtraTreesRegressor(bootstrap=True, max_features=0.15000000000000002, min_samples_leaf=9, min_samples_split=7,
n_estimators=100))
# Fix random state for all the steps in exported pipeline
set_param_recursive(exported_pipeline.steps, 'random_state', 42)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)

Thanks in advance