Questions on Using `nnx.value_and_grad` for Loss Calculation and Model Decoupling in Flax NNX

Hello everyone.

While implementing the A2C reinforcement learning algorithm using Flax NNX, I encountered some challenges and would appreciate your guidance. Below is a simplified code:

def run_epoch(policy_network , value_network , observation):

def data(policy_network, observation):
data = policy_network(observation)
return data

def compute_value(value_network, data):
value = value_network(data.observation)
return value

def compute_advantage(value):
advantage # Derived from a series of computations based on value
return advantage

def compute_policy_loss(policy_network, data, advantage):
policy_loss = -jnp.mean(jax.lax.stop_gradient(advantage) * data.log_prob)
return policy_loss

def compute_critic_loss(value_network, advantage):
critic_loss = jnp.mean(advantage**2)
return critic_loss

data = data(self.policy_network, observation)
value = compute_value(self.value_network, data)
advantage = compute_advantage(value)

policy_loss, policy_grad = nnx.value_and_grad(
compute_policy_loss, has_aux=False
)(self.policy_network, data, advantage)

value_loss, value_grad = nnx.value_and_grad(
compute_critic_loss, has_aux=False
)(self.value_network, advantage)

return policy_grad, value_grad

**Background**

- Both `policy_network` and `value_network` are complex models based on Transformer modules, and their forward pass involves multi-layer computational logic. These networks are implemented as `nnx.Module`.
- Since both loss functions require `advantage`, which depends on the forward pass of both `policy_network` and `value_network`, I attempted to extract the forward pass as a separate step. I then passed only the results of the forward pass to `compute_policy_loss` and `compute_critic_loss`.
- However, during execution, if the models are not passed directly to the loss functions, the code raises an error. In all examples in the documentation, `nnx.value_and_grad` seems to require passing the models directly to the loss function. As a workaround, I passed `policy_network` and `value_network` into `compute_policy_loss` and `compute_critic_loss`, and the code worked. However, the two`nnx.Module` instances are not explicitly used within these functions.

**Questions**

- Is it mandatory to pass the model to the loss function for proper gradient computation? Is it possible to use only the results of the forward pass in the loss calculation, without passing the entire model?
- Can the forward pass of the model be decoupled from the loss calculation? Specifically, can the forward pass be extracted as a separate step without affecting the proper functioning of `nnx.value_and_grad`?