Sure!
Before:
{'model': {'accuracy': {'name': 'accuracy', 'x': [0, 1], 'y': [0.5789473652839661, 1.0]}}
After:
{'model': {'accuracy': {'name': 'accuracy', 'x': [0, 1, 2 ], 'y': [0.5789473652839661, 1.0, 2.0 ]}}
Expected:
{'model': {'accuracy': {'name': 'accuracy', 'x': [ 0, 1], 'y': [ 2.0 , 1.0]}}

Indeed, does what stated in the docu, however I think its a bit odd, as .report_scalar() works quite different in this case compared to the normal case and iteration is not an optional param but will be ignored anyway

This seems to be in line with what you see

Hi NastyOtter17 , I'm not sure I understand - can you explain what you see in the UI after running this as opposed to what you expect to see?

NastyOtter17 from Task.init() 's docstring regarding continue_last_task :
` continue_last_task (bool ) – Continue the execution of a previously executed Task (experiment)

When continuing the executing of a previously executed Task, all previous artifacts / models/ logs are intact. New logs will continue iteration/step based on the previous-execution maximum iteration value. For example: The last train/loss scalar reported was iteration 100, the next report will be iteration 101. `