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daf78ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | # GROW checkpoints
The three checkpoints released here correspond to rows of **Table 1** in the
paper.
| Folder | Table 1 row | Model | Checkpoint | Notes |
|--------|-------------|-------|-----------|-------|
| `01_pretrain/` | Row 1 | Pretrained DiTAR (`DiTAR`) | `model_200000.pt` | 200K-step pretrain; also the frozen reference *q* for RL |
| `02_ditar_grpo_nfe10/` | Row 3 | DiTAR-GRPO, NFE=10 (`DiTAR_SDE`) | `model_750.pt` | Flow-GRPO baseline, rollout NFE=10, β<sub>W2</sub>=0, lr=1e-5 |
| `03_grow_nfe10/` | Row 6 | GROW, NFE=10 (`DiTAR_ORW`) | `model_750.pt` | **Default GROW setting**: rollout NFE=10, β<sub>W2</sub>=0.025, lr=2e-6 |
Reported full-test-set metrics (WER / SIM — LibriSpeech-PC · Seed-TTS EN · Seed-TTS ZH):
| Checkpoint | LS WER/SIM | Seed-EN WER/SIM | Seed-ZH WER/SIM |
|------------|-----------|-----------------|-----------------|
| `01_pretrain` | 2.373 / 0.648 | 2.406 / 0.663 | 1.269 / 0.717 |
| `02_ditar_grpo_nfe10` | 2.332 / 0.683 | 1.728 / 0.699 | 1.180 / 0.738 |
| `03_grow_nfe10` | 1.927 / 0.701 | 1.763 / 0.702 | 0.983 / 0.742 |
Each folder ships the training/inference `config.yaml` next to the
`.pt` weights. The eval loader reads model weights from `model_state_dict`
(RL checkpoints) or `ema_model_state_dict` (the pretrained checkpoint),
matching `cfg.model.name`.
## Layout
```
model_ckpts/
├── 01_pretrain/ config.yaml model_200000.pt
├── 02_ditar_grpo_nfe10/ config.yaml model_750.pt
└── 03_grow_nfe10/ config.yaml model_750.pt
```
> The `.pt` weights are large (4–13 GB) and are distributed via HuggingFace,
> not git. Download them into the matching folder
> before running evaluation.
|