diff --git a/docs/source/en/main_classes/callback.md b/docs/source/en/main_classes/callback.md
index ccfdf256832472..87bf0d63af1fc2 100644
--- a/docs/source/en/main_classes/callback.md
+++ b/docs/source/en/main_classes/callback.md
@@ -25,7 +25,7 @@ Callbacks are "read only" pieces of code, apart from the [`TrainerControl`] obje
 cannot change anything in the training loop. For customizations that require changes in the training loop, you should
 subclass [`Trainer`] and override the methods you need (see [trainer](trainer) for examples).
 
-By default a [`Trainer`] will use the following callbacks:
+By default, `TrainingArguments.report_to` is set to `"all"`, so a [`Trainer`] will use the following callbacks.
 
 - [`DefaultFlowCallback`] which handles the default behavior for logging, saving and evaluation.
 - [`PrinterCallback`] or [`ProgressCallback`] to display progress and print the
@@ -45,6 +45,8 @@ By default a [`Trainer`] will use the following callbacks:
 - [`~integrations.DagsHubCallback`] if [dagshub](https://dagshub.com/) is installed.
 - [`~integrations.FlyteCallback`] if [flyte](https://flyte.org/) is installed.
 
+If a package is installed but you don't wish to use the accompanying integration, you can change `TrainingArguments.report_to` to a list of just those integrations you want to use (e.g. `["azure_ml", "wandb"]`). 
+
 The main class that implements callbacks is [`TrainerCallback`]. It gets the
 [`TrainingArguments`] used to instantiate the [`Trainer`], can access that
 Trainer's internal state via [`TrainerState`], and can take some actions on the training loop via
diff --git a/src/transformers/training_args.py b/src/transformers/training_args.py
index 635ab656ff699c..96cb467bcbeb7c 100644
--- a/src/transformers/training_args.py
+++ b/src/transformers/training_args.py
@@ -2345,10 +2345,11 @@ def set_logging(
                 Logger log level to use on the main process. Possible choices are the log levels as strings: `"debug"`,
                 `"info"`, `"warning"`, `"error"` and `"critical"`, plus a `"passive"` level which doesn't set anything
                 and lets the application set the level.
-            report_to (`str` or `List[str]`, *optional*, defaults to `"none"`):
+            report_to (`str` or `List[str]`, *optional*, defaults to `"all"`):
                 The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,
-                `"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. Use `"all"` to report
-                to all integrations installed, `"none"` for no integrations.
+                `"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"flyte"`, `"mlflow"`, `"neptune"`,
+                `"tensorboard"`, and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no
+                integrations.
             first_step (`bool`, *optional*, defaults to `False`):
                 Whether to log and evaluate the first `global_step` or not.
             nan_inf_filter (`bool`, *optional*, defaults to `True`):