FPRM flagship L4 — two arms (Sudoku-Extreme)
Raw research checkpoints for two flagship runs. Accuracy numbers are not included here on purpose: they are re-measured with the frozen eval pipeline.
| Run | loop_attnres |
Final step | Size |
|---|---|---|---|
fl-b01-L4-lrdecay-s1 |
true (grid=ema, ema_heads=1, beta_init=0.1, mode=state, window=16) |
78120 / 78125 | 4.2 GB |
fl-carrylast-L4-s1 |
false |
78120 / 78125 | 5.8 GB |
IMPORTANT: use each run's own trm.py
The two arms were trained with different trm.py revisions (differing md5).
fl-carrylast-L4-s1's behavioural difference lives in its code, not in its
config or parameters — its state dict has no loop_attn.* params at any step
(verified at 5000 / 45570 / 78120, so no mid-run architecture change).
Each directory ships the exact trm.py, losses.py and all_config.yaml it
was trained with. Do not cross-load.
Shared config
data/sudoku-extreme-1k-aug-1000, hidden 512, L_layers=4, H_cycles=3,
L_cycles=6, halt_max_steps=16, rope, bf16, stablemax_cross_entropy,
q_loss_coeff=0.5. Full config in each all_config.yaml.
Files per run
step_<N>— model state dict only (109 MB)step_<N>_train_state.pt— model + optimizer, for resuming (273 MB)all_config.yaml,trm.py,losses.py
Training logs (logs/)
Both runs reached 78,125 steps and ended exit=0 / DONE. No NaN and no OOM
in either log. Both were restarted and resumed from checkpoints mid-training.
The b01 log contains 6 tracebacks, all infrastructure-level, none a training
failure: 4x ModuleNotFoundError: coolname (launch-env, fast-failed and
relaunched) and a disk-full (Errno 28) while writing step_65100 on
2026-07-17, after which the run resumed and completed.
Last logged train metrics (not eval):
| Run | lm_loss | seq_acc | q_halt_acc |
|---|---|---|---|
fl-b01-L4-lrdecay-s1 |
0.63375 | 0.8547 | 0.98291 |
fl-carrylast-L4-s1 |
0.64277 | 0.85833 | 1.0 |