ReduceLROnPlateau increases learning rate exponentially, causing training to diverge
Labels: triaged, module: LrScheduler

### 🐛 Describe the bug

I've been training some Segformer variants from HuggingFace via Lightning using the following optimizer setup.

```python
def configure_optimizers(self):

        from torch.optim.lr_scheduler import ReduceLROnPlateau
        optimizer = torch.optim.AdamW(self.parameters(), lr=self.hparams.learning_rate)

        return {
            "optimizer": optimizer,
            "lr_scheduler": {
                "scheduler": ReduceLROnPlateau(
                    optimizer,
                    mode="min",
                    verbose=True,
                    patience=self.hparams.learning_rate_schedule_patience,
                ),
                "monitor": "val/loss",
            },
        }
        return 
```

This has worked fine, apart from a recent run where the loss suddenly exploded. I'm not sure what the trigger was because the validation curve largely looked OK and at the point where the LR exploded, had even smoothed out quite nicely.

<img width="1411" alt="image" src="https://github.com/pytorch/pytorch/assets/3159591/932ab660-3459-461a-95a1-49aec76500f3">

Regardless of some instability in training, it seems like ReduceLROnPlateau progressively ramped the learning rate until it settles around 1 and everything else tanked with it.

Any idea why this occurred?

Here's a run where things worked as expected:

<img width="1376" alt="image" src="https://github.com/pytorch/pytorch/assets/3159591/5b0e876e-c564-4fb6-b556-e89ae906ba60">


### Versions

```
Collecting environment information...
PyTorch version: 1.12.1
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 10 (buster) (x86_64)
GCC version: (Debian 8.3.0-6) 8.3.0
Clang version: Could not collect
CMake version: version 3.13.4
Libc version: glibc-2.28

Python version: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:36:39) [GCC 10.4.0] (64-bit runtime)
Python platform: Linux-4.19.0-24-cloud-amd64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: 11.3.109
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: Tesla T4
GPU 1: Tesla T4

Nvidia driver version: 470.57.02
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
Address sizes:       46 bits physical, 48 bits virtual
CPU(s):              8
On-line CPU(s) list: 0-7
Thread(s) per core:  2
Core(s) per socket:  4
Socket(s):           1
NUMA node(s):        1
Vendor ID:           GenuineIntel
CPU family:          6
Model:               85
Model name:          Intel(R) Xeon(R) CPU @ 2.00GHz
Stepping:            3
CPU MHz:             2000.172
BogoMIPS:            4000.34
Hypervisor vendor:   KVM
Virtualization type: full
L1d cache:           32K
L1i cache:           32K
L2 cache:            1024K
L3 cache:            39424K
NUMA node0 CPU(s):   0-7
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities

Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.7.1
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.4
[pip3] pytorch-lightning==2.0.5
[pip3] segmentation-models-pytorch==0.3.0
[pip3] torch==1.12.1
[pip3] torch-tb-profiler==0.4.0
[pip3] torchaudio==0.12.1
[pip3] torchgeo==0.4.1
[pip3] torchmetrics==0.11.4
[pip3] torchvision==0.13.1
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               ha36c431_9    nvidia
[conda] efficientnet-pytorch      0.7.1                    pypi_0    pypi
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0           py310ha2c4b55_0    conda-forge
[conda] mkl_fft                   1.3.1           py310h2b4bcf5_1    conda-forge
[conda] mkl_random                1.2.2           py310h00e6091_0  
[conda] numpy                     1.23.4                   pypi_0    pypi
[conda] pytorch                   1.12.1          py3.10_cuda11.3_cudnn8.3.2_0    pytorch
[conda] pytorch-lightning         2.0.5                    pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] segmentation-models-pytorch 0.3.0                    pypi_0    pypi
[conda] torch-tb-profiler         0.4.0                    pypi_0    pypi
[conda] torchaudio                0.12.1              py310_cu113    pytorch
[conda] torchgeo                  0.4.1                    pypi_0    pypi
[conda] torchmetrics              0.11.4                   pypi_0    pypi
[conda] torchvision               0.13.1              py310_cu113    pytorch
```