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| 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, βW2=0, lr=1e-5 |
03_grow_nfe10/ | Row 6 | GROW, NFE=10 (DiTAR_ORW) | model_750.pt | Default GROW setting: rollout NFE=10, βW2=0.025, lr=2e-6 |
| 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 |
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.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.ptThe.ptweights are large (4–13 GB) and are distributed via HuggingFace, not git. Download them into the matching folder before running evaluation.