def train(self, buffer):
        """
        Perform a training update using minibatch GD.
        :param buffer: (SharedReplayBuffer) buffer containing training data.
        :param update_actor: (bool) whether to update actor network.

        :return train_info: (dict) contains information regarding training update (e.g. loss, grad norms, etc).
        """
        train_info = {}
        train_info['policy_loss'] = 0
        train_info['policy_grad_norm'] = 0

        update_time = 0
        for _ in range(self.ppo_epoch):
            data_generator = buffer.appo_sampler(self.num_mini_batch)
            for sample in data_generator:
                policy_loss, policy_grad_norm = self.ppo_update(sample)
                train_info['policy_loss'] += policy_loss
                train_info['policy_grad_norm'] += policy_grad_norm
                update_time += 1

        for k in train_info.keys():
            train_info[k] /= update_time
 
        return train_info

        Perform a training update using minibatch GD.
        :param buffer: (SharedReplayBuffer) buffer containing training data.
        :param update_actor: (bool) whether to update actor network.

        :return train_info: (dict) contains information regarding training update (e.g. loss, grad norms, etc).
        
train/mat/trainers/llm_trainer_grpo.py