How to reduce the RMSE in energy without losing accuracy in force

[lnnbig]

I trained my NNP model with the following loss parameters (see a full input.json file at the end):
"loss": {
"start_pref_e": 0.02,
"limit_pref_e": 1,
"start_pref_f": 1000,
"limit_pref_f": 1,
"start_pref_v": 10,
"limit_pref_v": 0.2
}.
The final energy and force error (RMSE) is 10-2 eV/atom and 10-1 eV/angstrom, as shown in the attached figure. I want to control the energy error below 10-3 eV/atom. Perhaps I can increase "limit_pref_e", but this means that I will sacrifice the accuracy in force.

Do you have any suggestions to reduce the RMSE in energy without losing accuracy in force?
Thanks very much.

input.json

{
"model": {
"descriptor": {
"type": "se_e2_a",
"sel": "auto",
"rcut_smth": 0.5,
"rcut": 6.5,
"neuron": [
25,
50,
100
],
"axis_neuron": 12,
"seed": 3482162051
},
"fitting_net": {
"neuron": [
240,
240,
240
],
"resnet_dt": true,
"seed": 3277049792
},
"type_map": [
"XXXX",
"XXXX"
]
},
"learning_rate": {
"type": "exp",
"decay_steps": 5000,
"start_lr": 0.001,
"stop_lr": 3.51e-08
},
"loss": {
"start_pref_e": 0.02,
"limit_pref_e": 1,
"start_pref_f": 1000,
"limit_pref_f": 1,
"start_pref_v": 10,
"limit_pref_v": 0.2
},
"training": {
"numb_steps": 300000,
"disp_file": "lcurve.out",
"disp_freq": 1000,
"save_freq": 10000,
"disp_training": true,
"time_training": true,
"profiling": false,
"profiling_file": "timeline.json",
"seed": 1799803115,
"training_data": {
"systems": [
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"../data.init/set25",
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[wanghan-iapcm]

Have you tried to use a larger E prefactor? How much force accuracy would you loss?