A role represents a group of workers in the same process. We have
pre-defined several roles in `ray_trainer.py <https://github.com/verl-project/verl/blob/main/verl/trainer/ppo/ray_trainer.py#L38>`_.

.. code:: python

   class Role(Enum):
       """
       To create more roles dynamically, you can subclass Role and add new members
       """
       Actor = 0  # This worker only has Actor
       Rollout = 1 # This worker only has Rollout
       ActorRollout = 2 # This worker has both actor and rollout, it's a HybridEngine
       Critic = 3 # This worker only has critic
       RefPolicy = 4 # This worker only has reference policy
       RewardModel = 5 # This worker only has reward model
       ActorRolloutRef = 6 # This worker contains actor, rollout and reference policy simultaneously 

Step 2: Define the worker class corresponding to this role

- Resource pool is a division of global GPU resources,
  ``resource_pool_spec`` is a dict, mapping from id to # of GPUs

  - In the above example, we defined a global resource pool:
    global_pool_id, and then put all roles on this one resource pool
    with all the GPUs in this post-training task. This refers to
    *co-locate* placement where all the models share the same set of
    GPUs.

- See resource pool and placement for advance usage.

Defining reward model/function
------------------------------

.. code:: python

   # we should adopt a multi-source reward function here
   # - for rule-based rm, we directly call a reward score
   # - for model-based rm, we call a model
   # - for code related prompt, we send to a sandbox if there are test cases
   # - finally, we combine all the rewards together
   # - The reward type depends on the tag of the data
   if config.reward_model.enable:
       from verl.workers.engine_workers import TrainingWorker
       role_worker_mapping[Role.RewardModel] = TrainingWorker
       mapping[Role.RewardModel] = global_pool_id
    
   reward_fn = RewardManager(tokenizer=tokenizer, num_examine=0)

   # Note that we always use function-based RM for validation
   val_reward_fn = RewardManager(tokenizer=tokenizer, num_examine=1)

   resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)

Since not all tasks use model-based RM, users need to define here
whether it's a model-based RM or a function-based RM

- If it's a model-based RM, directly add the ``RewardModel`` role in the
  resource mapping and add it to the resource pool mapping.

  - The default ``TrainingWorker`` handles reward models through the unified
    model engine and supports the typical huggingface
    ``AutoModelForSequenceClassification`` layout. For custom reward models
    you can subclass :class:`verl.workers.engine_workers.TrainingWorker`
    or build a dedicated worker on top of the `model engine package
    <https://github.com/verl-project/verl/blob/main/verl/workers/engine>`_.

- If it's a function-based RM, the users are required to classified the
  reward function for each datasets.

.. code:: python

   def _select_rm_score_fn(data_source):
       if data_source == 'openai/gsm8k':
           return gsm8k.compute_score
       elif data_source == 'lighteval/MATH':
           return math.compute_score
       else:
           raise NotImplementedError

See reward functions implemented in `directory <https://github.com/verl-project/verl/blob/main/verl/utils/reward_score/>`_ 
for more information.

Define, init and run the PPO Trainer
------------------------------------

.. code:: python

   trainer = RayPPOTrainer(config=config,
                           tokenizer=tokenizer,
                           role_worker_mapping=role_worker_mapping,
                           resource_pool_manager=resource_pool_manager,
                           ray_worker_group_cls=ray_worker_group_cls,
                           reward_fn=reward_fn,
                           val_reward_fn=val_reward_fn)
   trainer.init_workers()
   trainer.fit()

- We first initialize the ``RayPPOTrainer`` with user config, tokenizer
  and all the above worker mapping, resource pool, worker group and
  reward functions
- We first call the ``trainer.init_workers()`` to initialize the models
  on the allocated GPUs (in the resource pool)
- The actual PPO training will be executed in ``trainer.fit()``

verl can be easily extended to other RL algorithms by reusing the Ray
model workers, resource pool and reward functions. See :doc:`extension<../advance/dpo_extension>` for
more information.

Details of the ``RayPPOTrainer`` is discussed in :doc:`Ray Trainer<../workers/ray_trainer>`.