---

This section details on the original issue you should resolve

<issue_title>ReduceLROnPlateu within configure_optimizers behave abnormally</issue_title>
<issue_description>### Bug description

Got error

  File "c:\Users\sean\miniconda3\envs\keras+torch+pl\Lib\site-packages\lightning\pytorch\loops\training_epoch_loop.py", line 459, in _update_learning_rates
    raise MisconfigurationException(
lightning.fabric.utilities.exceptions.MisconfigurationException: ReduceLROnPlateau conditioned on metric val/loss which is not available. Available metrics are: ['lr-AdamW/pg1', 'lr-AdamW/pg2', 'train/a_pcc', 'train/loss']. Condition can be set using `monitor` key in lr scheduler dict

Here is the configure_optimizers function:

    @final
    def configure_optimizers(self):

        decay, no_decay = [], []
        for name, param in self.named_parameters():
            if not param.requires_grad:
                continue
            if "bias" in name or "Norm" in name:
                no_decay.append(param)
            else:
                decay.append(param)

        grouped_params = [
            {"params": decay, "weight_decay": self.weight_decay, "lr": self.lr * 0.3},
            {
                "params": no_decay,
                "weight_decay": self.weight_decay,
                "lr": self.lr * 1.7,
            },
        ]

        optimizer = self.optmizer_class(
            grouped_params, lr=self.lr, weight_decay=self.weight_decay
        )

        scheduler = self.lr_scheduler_class(
            optimizer, **self.lr_scheduler_args if self.lr_scheduler_args else {}
        )
        scheduler = {
            "scheduler": self.lr_scheduler_class(
                optimizer, **self.lr_scheduler_args if self.lr_scheduler_args else {}
            ),
            "monitor": "val/loss",
            "interval": "epoch",
            "frequency": 1,
            # "strict": False,
        }
        return {"optimizer": optimizer, "lr_scheduler": scheduler}

The lr_scheduler_class is passed in as

  lr_scheduler_class: torch.optim.lr_scheduler.ReduceLROnPlateau
  lr_scheduler_args:
    mode: min
    factor: 0.5
    patience: 10
    threshold: 0.0001
    threshold_mode: rel
    cooldown: 5
    min_lr: 1.e-9
    eps: 1.e-08

(using yaml and CLI, which, I think, is not the case here)

It seems that I got the error at the end of the training epoch, as I just see the progress bar reports train/loss. The validation epoch is not finished, but the scheduler is called.

I am quite sure that val/loss is available after validation epoch is finished, because progress bar can correctly display it.

What version are you seeing the problem on?

v2.5

Reproduced in studio

No response

How to reproduce the bug

Error messages and logs

# Error messages and logs here please

Environment

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More info

No response</issue_description>

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