def fit(self):
        """
        The training loop of PPO.
        The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow.
        The light-weight advantage computation is done on the driver process.
        """
        from verl.utils.tracking import Tracking
        from omegaconf import OmegaConf

        logger = Tracking(project_name=self.config.trainer.project_name,
                          experiment_name=self.config.trainer.experiment_name,
                          default_backend=self.config.trainer.logger,
                          config=OmegaConf.to_container(self.config, resolve=True))

        self.global_steps = 0

        # perform validation before training
        # currently, we only support validation using the reward_function.
        if self.val_reward_fn is not None and self.config.trainer.get('val_before_train', True):
            val_metrics = self._validate()
            pprint(f'Initial validation metrics: {val_metrics}')
            logger.log(data=val_metrics, step=self.global_steps)
            if self.config.trainer.get('val_only', False):
                return

        # we start from step 1
        self.global_steps += 1

        for epoch in range(self.config.trainer.total_epochs):
            for batch_dict in self.train_dataloader:
                print(f'epoch {epoch}, step {self.global_steps}')
                metrics = {}
                timing_raw = {}

                batch: DataProto = DataProto.from_single_dict(batch_dict)

                # pop those keys for generation
                gen_batch = batch.pop(batch_keys=['input_ids', 'attention_mask', 'position_ids'])

                with _timer('step', timing_raw):
                    # generate a batch
                    with _timer('gen', timing_raw):
                        gen_batch_output = self.actor_rollout_wg.generate_sequences(gen_batch)

                    batch.non_tensor_batch['uid'] = np.array([str(uuid.uuid4()) for _ in range(len(batch.batch))],
                                                             dtype=object)
                    # repeat to align with repeated responses in rollout
                    batch = batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True)
                    batch = batch.union(gen_batch_output)

                    # balance the number of valid tokens on each dp rank.
                    # Note that this breaks the order of data inside the batch.
                    # Please take care when you implement group based adv computation such as GRPO and rloo
                    self._balance_batch(batch, metrics=metrics)

                    # compute global_valid tokens
                    batch.meta_info['global_token_num'] = torch.sum(batch.batch['attention_mask'], dim=-1).tolist()

                    if self.use_reference_policy:
                        # compute reference log_prob
                        with _timer('ref', timing_raw):
                            ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch)
                            batch = batch.union(ref_log_prob)

                    # compute values
                    if self.use_critic:
                        with _timer('values', timing_raw):
                            values = self.critic_wg.compute_values(batch)
                            batch = batch.union(values)

                    with _timer('adv', timing_raw):
                        # compute scores. Support both model and function-based.
                        # We first compute the scores using reward model. Then, we call reward_fn to combine
                        # the results from reward model and rule-based results.
                        if self.use_rm:
                            # we first compute reward model score
                            reward_tensor = self.rm_wg.compute_rm_score(batch)
                            batch = batch.union(reward_tensor)

                        # we combine with rule-based rm
                        reward_tensor = self.reward_fn(batch)
                        batch.batch['token_level_scores'] = reward_tensor

                        # compute rewards. apply_kl_penalty if available
                        if not self.config.actor_rollout_ref.actor.use_kl_loss:
                            batch, kl_metrics = apply_kl_penalty(batch,
                                                                 kl_ctrl=self.kl_ctrl,
                                                                 kl_penalty=self.config.algorithm.kl_penalty)
                            metrics.update(kl_metrics)
                        else:
                            batch.batch['token_level_rewards'] = batch.batch['token_level_scores']

                        # compute advantages, executed on the driver process
                        batch = compute_advantage(batch,
                                                  adv_estimator=self.config.algorithm.adv_estimator,
                                                  gamma=self.config.algorithm.gamma,
                                                  lam=self.config.algorithm.lam,
                                                  num_repeat=self.config.actor_rollout_ref.rollout.n)

                    # update critic
                    if self.use_critic:
                        with _timer('update_critic', timing_raw):
                            critic_output = self.critic_wg.update_critic(batch)
                        critic_output_metrics = reduce_metrics(critic_output.meta_info['metrics'])
                        metrics.update(critic_output_metrics)

                    # implement critic warmup
                    if self.config.trainer.critic_warmup <= self.global_steps:
                        # update actor
                        with _timer('update_actor', timing_raw):
                            actor_output = self.actor_rollout_wg.update_actor(batch)
                        actor_output_metrics = reduce_metrics(actor_output.meta_info['metrics'])
                        metrics.update(actor_output_metrics)

                    # validate
                    if self.val_reward_fn is not None and self.config.trainer.test_freq > 0 and \
                        self.global_steps % self.config.trainer.test_freq == 0:
                        with _timer('testing', timing_raw):
                            val_metrics: dict = self._validate()
                        metrics.update(val_metrics)

                    if self.config.trainer.save_freq > 0 and \
                            self.global_steps % self.config.trainer.save_freq == 0:
                        with _timer('save_checkpoint', timing_raw):
                            self._save_checkpoint()

                # collect metrics
                metrics.update(compute_data_metrics(batch=batch, use_critic=self.use_critic))
                metrics.update(compute_timing_metrics(batch=batch, timing_raw=timing_raw))

                # TODO: make a canonical logger that supports various backend
                logger.log(data=metrics, step=self.global_steps)

                self.global_steps += 1

                if self.global_steps >= self.total_training_steps:

                    # perform validation after training
                    if self.val_reward_fn is not None:
                        val_metrics = self._validate()
                        pprint(f'Final validation metrics: {val_metrics}')
                        logger.log(data=val_metrics, step=self.global_steps)
                    return

        The training loop of PPO.
        The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow.
        The light-weight advantage computation is done on the driver process.
        
verl/trainer/ppo/ray_trainer.py