Upload README.md

README.md

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+# 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.