# 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n	r	o_output	ype**](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n	r	o_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/_pydantic_types/forward_backward_output.py#L9)_]) – List of per-datum dicts mapping field names to [`TensorData`](https://tinker-docs.thinkingmachines.ai/tinker/api-reference/types/tensordata/).
- [**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.