# Split-Expert Bring-Up (`2026-03-10`)

This bundle captures the initial PyTorch bring-up for the new packed TWIN split-action-expert path on `pi0.5`.

Included here:

- exact split warm-start checkpoints created from the original single-head PyTorch base checkpoint
- invariant-check outputs for `split_independent` and `split_communicating`
- detached real-data smoke and `20`-step training logs on `lsnu/twin_dual_push_128_train`
- reproducibility commands used for the bring-up

## Warm-start summary

Both split modes inherit the same base expert weights and per-arm input/output projections from the single-head checkpoint.

- `split_independent`
  - `left_expert_max_abs_diff = 0.0`
  - `right_expert_max_abs_diff = 0.0`
  - `left_input_projection_max_abs_diff = 0.0`
  - `right_input_projection_max_abs_diff = 0.0`
  - `left_output_projection_max_abs_diff = 0.0`
  - `right_output_projection_max_abs_diff = 0.0`
- `split_communicating`
  - same exact inherited diffs as above
  - added cross-arm communication parameters are zero-initialized at warm start

## Real-data bring-up summary

Dataset used for real-data smoke and short training:

- `lsnu/twin_dual_push_128_train`

Successful detached runs:

- `split_independent_real_smoke3_r2`
  - `3` train steps on real packed TWIN data
  - checkpoint saved at step `3`
- `split_communicating_real_smoke3`
  - `3` train steps on real packed TWIN data
  - checkpoint saved at step `3`
- `split_independent_real_train20`
  - `20` train steps on real packed TWIN data
  - final logged train loss at step `20`: `0.6038`
  - checkpoint saved at step `20`
- `split_communicating_real_train20`
  - `20` train steps on real packed TWIN data
  - final logged train loss at step `20`: `0.5943`
  - checkpoint saved at step `20`

## Layout

- `bootstrap_checkpoints/`
  - exact split warm-start checkpoints
- `sanity_checks/`
  - invariant-check outputs
- `run_logs/`
  - detached real-data run logs
- `repro/commands_bringup.sh`
  - reproduction commands used during the bring-up