Flaml/LightGBM - Shouldn't I found better/faster or equal results from FLAML than direct LightGBM?

Hello

I have the following training with LightGBM

model2 = lgb.LGBMRegressor(learning_rate=0.09,max_depth=-5,random_state=42, n_estimators=20000) #, use_missing=FALSE)
model2.fit(x_train,y_train,eval_set=[(x_test,y_test),(x_train,y_train)], verbose=20,eval_metric='logloss') #, init_model=filename2_)

When I look at results it reutrn this

print('Training accuracy {:.4f}'.format(model2.score(x_train,y_train)))
print('Testing accuracy {:.4f}'.format(model2.score(x_test,y_test)))

lgb.plot_importance(model2)
lgb.plot_metric(model2)

Training accuracy 1.0000
Testing accuracy 0.7628

I'm trying with FLAML (please keep in mind I'm new with Python and those libraries) I would expect the results to get better quicker?

from flaml import AutoML
automl = AutoML()
automl.fit(X_train=x_train, y_train=y_train, time_budget=60*60*7, estimator_list=['lgbm'], task='regression')#, estimator_list=['lgbm'], task='regression') #'regression classification

[1.76959344 0.11876356 1.6142814  ... 1.79535421 0.55507853 1.04489782]
LGBMRegressor(colsample_bytree=0.7114672034208275,
              learning_rate=0.013851620085123617, max_bin=1023,
              min_child_samples=9, n_estimators=1877, num_leaves=262,
              reg_alpha=0.009443894441159862, reg_lambda=1.8437202926962308,
              verbose=-1)
Training accuracy 0.9081
Testing accuracy 0.0693

TY!