# tinker.types.ForwardBackwardOutput

## *class* [**tinker.types.ForwardBackwardOutput**](https://github.com/thinking-machines-lab/tinker/blob/main/src/tinker/types/forward_backward_output.py#L12)()

**Fields:**

- [**loss_fn_output_type**](https://github.com/thinking-machines-lab/tinker/blob/main/src/tinker/types/forward_backward_output.py#L13) (*str*) – The class name of the loss function output records (e.g., 'TorchLossReturn', 'ArrayRecord').

- [**loss_fn_outputs**](https://github.com/thinking-machines-lab/tinker/blob/main/src/tinker/types/forward_backward_output.py#L16) (*List\[[LossFnOutput](https://github.com/thinking-machines-lab/tinker/blob/main/src/tinker/types/loss_fn_output.py#L9)\]*) – List of per-datum dicts mapping field names to [`TensorData`](https://tinker-docs.thinkingmachines.ai/tinker/api-reference/types/tensordata/index.md).

- [**metrics**](https://github.com/thinking-machines-lab/tinker/blob/main/src/tinker/types/forward_backward_output.py#L19) (*Dict[str, float]*) – Training metrics as key-value pairs.

  The following metrics are recorded only during MoE (Mixture of Experts) training.

  - `e_frac_with_tokens:mean`: Fraction of experts that received at least one token, averaged across layers. A value of 1.0 means every expert got work; 0.5 means half were idle. Decreasing over time is concerning (routing collapse).
  - `e_frac_oversubscribed:mean`: Fraction of experts receiving more tokens than perfect balance, averaged across layers. Increasing over time is concerning.
  - `e_max_violation:mean`: How much the most overloaded expert exceeds perfect balance, as a fraction of perfect balance, averaged across layers. Computed as `(max_tokens - perfect_balance) / perfect_balance`. A value of 2.0 means the busiest expert got 3x the fair share. Increasing over time is concerning.
  - `e_max_violation:max`: Same as `e_max_violation:mean` but takes the max across layers instead of the mean.
  - `e_min_violation:mean`: How much the least loaded expert is below perfect balance, as a fraction of perfect balance, averaged across layers. Typically negative; decreasing (more negative) is concerning.