Index libero-all-suites/init_variants/ffn_magnitude

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---
language: en
tags:
- robotics
- libero
- loopwam
- world-action-model
---
# LoopWAM final checkpoints

Final trained policy weights from the LoopWAM experiments. This repository contains **25 checkpoints**, separated by training data scope. Both original and repeated 4/4 runs are retained. All production checkpoints are from completed ten-epoch runs with global batch 128 and training seed 42.

## October 9 additions: all-loop KV

| Directory | KV mode | Seed42/43/44 | Pooled SR |
|---|---|---|---:|
| [libero-long/v0-video4-action1-kv-concat](libero-long/v0-video4-action1-kv-concat) | concat | 83/86/86 | 255/300 = 85.00% |
| [libero-long/v0-video4-action1-kv-mix](libero-long/v0-video4-action1-kv-mix) | mix | 90/89/87 | 266/300 = 88.67% |

**These two additions require the [KV_concat code branch](https://github.com/anhdao69/LoopWAM_NT/tree/KV_concat), revision3a89bfac5cfccad66bfebbe00014f7f739a78adc or compatible later.**
Their checkpoints record action_kv_mode; the original LoopWAM_NT code predates
these modes. Use eager BF16 inference, as native Inductor failed numerical
equivalence. Each directory includes normalization, training provenance,
three evaluation summaries and a tensor-verified optimizer-free native policy.
The Long aligned4/1 and both original/repeat4/4 weights were hash-checked and
are unchanged. Their additional evaluation seeds do not create new weights.
The full-suite v0 4/1 training-only checkpoint is now included at `libero-all-suites/v0-video4-action1`. It has not been evaluated after training.
Expanded seed42: concat404/500 versus aligned413/500 (paired p=0.4814). The
original three-seed decision remains inconclusive. This grid overlaps the
original states and must not be pooled as independent evidence.
See the [updated results report](https://github.com/anhdao69/LoopWAM_NT/blob/KV_concat/plans/performance/LoopWAM_v0_V4A1_KVconcat_20261008.md).
## October 10 addition: full-suite aligned 1/4
[libero-all-suites/v0-video1-action4](libero-all-suites/v0-video1-action4)
contains the final ten-epoch full-suite v0 aligned 1/4 policy (21,700 updates,
global batch 128, training seed 42). Training took 10h 58m 46s on two H100s.
**Not evaluated:** this run finished under the requested training-only workflow.
Normalization, training metadata, fairness checks and export hashes are included.
## October 10 addition: full-suite 4/1
| Directory | Model | Video/action loops | Parameters | Evaluation |
|---|---|---:|---:|---|
| [libero-all-suites/v0-video4-action1](libero-all-suites/v0-video4-action1) | v0 | 4/1 | 584,536,135 | Training only; evaluation not run |
The policy completed ten epochs on the four-suite full dataset with global batch 128. Evaluation results are not available for this checkpoint.
## October 10 addition: full-suite 2/2
| Directory | Model | Video/action loops | Parameters | Evaluation |
|---|---|---:|---:|---|
| [libero-all-suites/v0-video2-action2](libero-all-suites/v0-video2-action2) | v0 | 2/2 | 584,536,135 | Training only; evaluation not run |
The checkpoint completed ten epochs on the four-suite full dataset with global batch 128. Evaluation has not been run.
## October 11 addition: full-suite mix 4/1
| Directory | Model | Video/action loops | Parameters | Evaluation |
|---|---|---:|---:|---|
| [libero-all-suites/v0-video4-action1-kv-mix](libero-all-suites/v0-video4-action1-kv-mix) | v0 | 4/1 mix | 584,536,423 | Training only; evaluation not run |
The checkpoint completed ten epochs on the four-suite full dataset with global batch 128. Evaluation has not been run.
## October 2026 additions: full-suite dense controls
| Directory | Model | Video/action loops | Parameters | Evaluation |
|---|---|---:|---:|---|
| [libero-all-suites/dense-s30](libero-all-suites/dense-s30) | dense_s30 | 1/1 | 1,416,114,247 | Training only; evaluation not run |
| [libero-all-suites/dense-s12](libero-all-suites/dense-s12) | dense_s12 | 1/1 | 584,536,135 | Training only; evaluation not run |
Dense-S12 / Dense-S30 trained on all four suites with the same data, normalization, initialization and schedule as `libero-all-suites/v0-video4-action4` (21,700 updates, global batch 128, seed 42) on two B200 GPUs. Not evaluated.
## October 2026 additions: v0 initialization variants
| Directory | Model | Video/action loops | Parameters | Evaluation |
|---|---|---:|---:|---|
| [libero-all-suites/init_variants/core_mean](libero-all-suites/init_variants/core_mean) | v0 init `core_mean` | 4/4 | 584,536,135 | Training only; evaluation not run |
| [libero-all-suites/init_variants/core16_21](libero-all-suites/init_variants/core16_21) | v0 init `core16_21` | 4/4 | 584,536,135 | Training only; evaluation not run |
| [libero-all-suites/init_variants/no_video_pretraining](libero-all-suites/init_variants/no_video_pretraining) | v0 init `no_video_pretraining` (no Wan video pretraining) | 4/4 | 584,536,135 | Training only; evaluation not run |
| [libero-all-suites/init_variants/core_random](libero-all-suites/init_variants/core_random) | v0 init `core_random` | 4/4 | 584,536,135 | 82.20% (4,932/6,000; 4 suites x seeds 42/43/44) |
| [libero-all-suites/init_variants/ffn_magnitude](libero-all-suites/init_variants/ffn_magnitude) | v0 init `ffn_magnitude` | 4/4 | 584,536,135 | 92.12% (5,527/6,000; 4 suites x seeds 42/43/44) |

v0 (video 4 / action 4) retrained on all four suites with the same data, normalization, schedule and precision as `libero-all-suites/v0-video4-action4` (21,700 updates, global batch 128, seed 42) on two B200 GPUs; only the student initialization differs (see each directory's README and `init_variant_manifest.json`). Not evaluated. `init_variants/no_video_pretraining` starts without any Wan video pretraining and was trained on two H100s (2 GPUs x microbatch 8 x accumulation 8). `init_variants/core_random` was trained on two H100s (2 GPUs x microbatch 8 x accumulation 8) and scored 82.20% on the matched 6,000-episode four-suite evaluation. `init_variants/ffn_magnitude` was trained on two H100s (2 GPUs x microbatch 8 x accumulation 8) and scored 92.12% on the matched 6,000-episode four-suite evaluation.

## October 26 addition: full-suite aligned 4/2

| Directory | Model | Video/action loops | Parameters | Evaluation |
|---|---|---:|---:|---|
| [libero-all-suites/v0-video4-action2](libero-all-suites/v0-video4-action2) | v0 aligned | 4/2 | 584,536,135 | Training only; evaluation not run |

Aligned (late) 4/2 v0 trained on all four suites with the same data, normalization, initialization recipe, schedule, precision and 2-GPU microbatch-8 layout as `libero-all-suites/v0-video4-action1` (21,700 updates, global batch 128, seed 42) on two H100s. Uses the LoopWAM_NT branch (revision `56e3328`). Not evaluated.
## Checkpoint index
| Directory | Model | Video/action loops | Parameters | Final evaluation |
|---|---|---|---:|---|
| [libero-all-suites/init_variants/core_mean](libero-all-suites/init_variants/core_mean) | v0 init `core_mean` | 4/4 | 584,536,135 | Training only; evaluation not run |
| [libero-all-suites/v0-video4-action2](libero-all-suites/v0-video4-action2) | v0 aligned | 4/2 | 584,536,135 | Training only; evaluation not run |
| [libero-all-suites/init_variants/core16_21](libero-all-suites/init_variants/core16_21) | v0 init `core16_21` | 4/4 | 584,536,135 | Training only; evaluation not run |
| [libero-all-suites/init_variants/no_video_pretraining](libero-all-suites/init_variants/no_video_pretraining) | v0 init `no_video_pretraining` (no Wan video pretraining) | 4/4 | 584,536,135 | Training only; evaluation not run |
| [libero-all-suites/init_variants/core_random](libero-all-suites/init_variants/core_random) | v0 init `core_random` | 4/4 | 584,536,135 | 82.20% (4,932/6,000; 4 suites x seeds 42/43/44) |
| [libero-all-suites/init_variants/ffn_magnitude](libero-all-suites/init_variants/ffn_magnitude) | v0 init `ffn_magnitude` | 4/4 | 584,536,135 | 92.12% (5,527/6,000; 4 suites x seeds 42/43/44) |
| [libero-all-suites/dense-s30](libero-all-suites/dense-s30) | dense_s30 | 1/1 | 1,416,114,247 | Training only; evaluation not run |
| [libero-all-suites/dense-s12](libero-all-suites/dense-s12) | dense_s12 | 1/1 | 584,536,135 | Training only; evaluation not run |
| [libero-all-suites/v0-video1-action4](libero-all-suites/v0-video1-action4) | v0 aligned | 1/4 | 584536135 | Not evaluated (training only) |
| [libero-long/v0-video4-action4-original](libero-long/v0-video4-action4-original) | v0 | 4/4 | 584,536,135 | 96/100 (96%) |
| [libero-long/v1-video4-action4](libero-long/v1-video4-action4) | v1 | 4/4 | 584,536,135 | 82/100 (82%) |
| [libero-long/dense-s12](libero-long/dense-s12) | dense_s12 | 1/1 | 584,536,135 | 81/100 (81%) |
| [libero-long/v2-video4-action4](libero-long/v2-video4-action4) | v2 | 4/4 | 584,536,135 | 81/100 (81%) |
| [libero-long/dense-s30](libero-long/dense-s30) | dense_s30 | 1/1 | 1,416,114,247 | 86/100 (86%) |
| [libero-all-suites/v0-video4-action4](libero-all-suites/v0-video4-action4) | v0 | 4/4 | 584,536,135 | 388/400 (97%) |
| [libero-long/v0-video4-action1](libero-long/v0-video4-action1) | v0 | 4/1 | 584,536,135 | 81/100 (81%) |
| [libero-long/v0-video4-action2](libero-long/v0-video4-action2) | v0 | 4/2 | 584,536,135 | 86/100 (86%) |
| [libero-long/v0-video2-action2](libero-long/v0-video2-action2) | v0 | 2/2 | 584,536,135 | 81/100 (81%) |
| [libero-long/v0-video1-action4](libero-long/v0-video1-action4) | v0 | 1/4 | 584,536,135 | 86/100 (86%) |
| [libero-long/v0-video4-action4-repeat](libero-long/v0-video4-action4-repeat) | v0 | 4/4 | 584,536,135 | 91/100 (91%) |

`libero-long/`: matched 344-training / 44-validation demonstration split, 7,250 updates. Dense-S12/S30 execute their independent blocks once; their 1/1 metadata does not mean the dense networks have equal depth. S12 has 12 independent block pairs; S30 has 30. Loop models use three prelude, six shared core and three coda pairs, with 3 + 6K + 3 effective block applications per expert.

`libero-all-suites/`: one v0 trained on all 1,712 locally available Spatial/Object/Goal/Long demonstrations, 21,700 updates. Its 388/400 result aggregates all four suites, so it is a different training/evaluation scope from the Long-only rows. The same four-suite checkpoint also scored 720/2,000 (36%) on LIBERO-Pro.

The original checkpoint-index rows below use 100 final-checkpoint rollouts at evaluation seed 42; the new KV table above reports three seeds. Standard-suite evaluations use 700 policy steps, 30 settling steps, 32-action predictions, replanning every ten actions, ten denoising steps and CFG 1. The original 4/4 checkpoint additionally scored 96%, 88%, 91% across evaluation seeds 42/43/44. These are one training seed and cannot establish across-training-seed significance.

## Files and integrity

Each directory contains `policy.pt`, `dataset_stats.json`, `data_manifest.json`, `training_manifest.json`, `training_timing.json`, `evaluation_summary.json`, and `export.json`. `policy.pt` is the native `loopwam-s-v1` checkpoint, containing FP32 model tensors, architecture/depth metadata and the training contract. Adam optimizer states are omitted; use it for inference or fresh-optimizer fine-tuning, not exact optimizer-state resume. All exported model tensors were checked for exact equality with the evaluated final checkpoint. `export.json` records both the original checkpoint SHA-256 and the exported file SHA-256; these hashes differ because optimizer removal changes serialization.

The frozen VAE and text encoder/embeddings are external inference dependencies. The native loader does not require the donor initialization artifact when `checkpoint_path` is provided. Normalization files are specific to each checkpoint and must remain matched. Videos, dataset videos and original optimizer checkpoints remain on the training server.

## Loading

Use the compatible [LoopWAM code](https://github.com/anhdao69/LoopWAM_NT/tree/LoopWAM_NT), at revision `8a29ffdce1537409e2b7e975e838d990ebbe068a` or a compatible later revision. This is a custom policy, not a Transformers AutoModel checkpoint. Install that repository’s inference dependencies first. The repository is publicly readable.

```python
import torch
from huggingface_hub import hf_hub_download
from fastwam.models.wan22.loopwam import create_loopwam
repo = "anhdao69/LoopWAM_NT"
variant = "libero-long/v0-video4-action4-repeat"
checkpoint = hf_hub_download(repo, f"{variant}/policy.pt")
stats = hf_hub_download(repo, f"{variant}/dataset_stats.json")
vae = hf_hub_download("Wan-AI/Wan2.1-T2V-1.3B", "Wan2.1_VAE.pth")
model = create_loopwam(checkpoint_path=checkpoint, vae_path=vae,
                       model_dtype=torch.float32, device="cuda").eval()
```

Depth/version are reconstructed from checkpoint metadata. Use the repository’s observation adapter, matched normalization, text embeddings with their padding masks, and BF16 autocast during prediction. See `scripts/evaluate_loopwam_libero.py` for the complete simulator pipeline. Evaluation summaries retain the original checkpoint hashes; use the tensor equality and source/export mapping in `export.json` when auditing exports. The evaluator computes the exported file’s own hash for a new evaluation.

## Provenance and rights

These research checkpoints derive from pretrained Wan and the FastWAM/LoopWAM implementation. Consult the upstream model and code licenses before redistribution or use; this model card does not grant additional upstream rights. The repository is currently public.

For detailed comparison caveats, runtime accounting and per-task results, see the [consolidated report](https://github.com/anhdao69/LoopWAM_NT/blob/LoopWAM_NT/plans/performance/LoopWAM_Consolidated_Results_20261008.md). That report’s earlier snapshot labels the repeat pending; the repeat is now complete at 91/100, as recorded here.

	---
	language: en
	tags:
	- robotics
	- libero
	- loopwam
	- world-action-model
	---

	# LoopWAM final checkpoints

	Final trained policy weights from the LoopWAM experiments. This repository contains 25 checkpoints, separated by training data scope. Both original and repeated 4/4 runs are retained. All production checkpoints are from completed ten-epoch runs with global batch 128 and training seed 42.

	## October 9 additions: all-loop KV

	\| Directory \| KV mode \| Seed42/43/44 \| Pooled SR \|
	\|---\|---\|---\|---:\|
	\| [libero-long/v0-video4-action1-kv-concat](libero-long/v0-video4-action1-kv-concat) \| concat \| 83/86/86 \| 255/300 = 85.00% \|
	\| [libero-long/v0-video4-action1-kv-mix](libero-long/v0-video4-action1-kv-mix) \| mix \| 90/89/87 \| 266/300 = 88.67% \|

	These two additions require the [KV_concat code branch](https://github.com/anhdao69/LoopWAM_NT/tree/KV_concat), revision3a89bfac5cfccad66bfebbe00014f7f739a78adc or compatible later.
	Their checkpoints record action_kv_mode; the original LoopWAM_NT code predates
	these modes. Use eager BF16 inference, as native Inductor failed numerical
	equivalence. Each directory includes normalization, training provenance,
	three evaluation summaries and a tensor-verified optimizer-free native policy.
	The Long aligned4/1 and both original/repeat4/4 weights were hash-checked and
	are unchanged. Their additional evaluation seeds do not create new weights.
	The full-suite v0 4/1 training-only checkpoint is now included at `libero-all-suites/v0-video4-action1`. It has not been evaluated after training.

	Expanded seed42: concat404/500 versus aligned413/500 (paired p=0.4814). The
	original three-seed decision remains inconclusive. This grid overlaps the
	original states and must not be pooled as independent evidence.
	See the [updated results report](https://github.com/anhdao69/LoopWAM_NT/blob/KV_concat/plans/performance/LoopWAM_v0_V4A1_KVconcat_20261008.md).

	## October 10 addition: full-suite aligned 1/4

	[libero-all-suites/v0-video1-action4](libero-all-suites/v0-video1-action4)
	contains the final ten-epoch full-suite v0 aligned 1/4 policy (21,700 updates,
	global batch 128, training seed 42). Training took 10h 58m 46s on two H100s.
	Not evaluated: this run finished under the requested training-only workflow.
	Normalization, training metadata, fairness checks and export hashes are included.

	## October 10 addition: full-suite 4/1

	\| Directory \| Model \| Video/action loops \| Parameters \| Evaluation \|
	\|---\|---\|---:\|---:\|---\|
	\| [libero-all-suites/v0-video4-action1](libero-all-suites/v0-video4-action1) \| v0 \| 4/1 \| 584,536,135 \| Training only; evaluation not run \|

	The policy completed ten epochs on the four-suite full dataset with global batch 128. Evaluation results are not available for this checkpoint.

	## October 10 addition: full-suite 2/2

	\| Directory \| Model \| Video/action loops \| Parameters \| Evaluation \|
	\|---\|---\|---:\|---:\|---\|
	\| [libero-all-suites/v0-video2-action2](libero-all-suites/v0-video2-action2) \| v0 \| 2/2 \| 584,536,135 \| Training only; evaluation not run \|

	The checkpoint completed ten epochs on the four-suite full dataset with global batch 128. Evaluation has not been run.

	## October 11 addition: full-suite mix 4/1

	\| Directory \| Model \| Video/action loops \| Parameters \| Evaluation \|
	\|---\|---\|---:\|---:\|---\|
	\| [libero-all-suites/v0-video4-action1-kv-mix](libero-all-suites/v0-video4-action1-kv-mix) \| v0 \| 4/1 mix \| 584,536,423 \| Training only; evaluation not run \|

	The checkpoint completed ten epochs on the four-suite full dataset with global batch 128. Evaluation has not been run.

	## October 2026 additions: full-suite dense controls

	\| Directory \| Model \| Video/action loops \| Parameters \| Evaluation \|
	\|---\|---\|---:\|---:\|---\|
	\| [libero-all-suites/dense-s30](libero-all-suites/dense-s30) \| dense_s30 \| 1/1 \| 1,416,114,247 \| Training only; evaluation not run \|
	\| [libero-all-suites/dense-s12](libero-all-suites/dense-s12) \| dense_s12 \| 1/1 \| 584,536,135 \| Training only; evaluation not run \|

	Dense-S12 / Dense-S30 trained on all four suites with the same data, normalization, initialization and schedule as `libero-all-suites/v0-video4-action4` (21,700 updates, global batch 128, seed 42) on two B200 GPUs. Not evaluated.

	## October 2026 additions: v0 initialization variants

	\| Directory \| Model \| Video/action loops \| Parameters \| Evaluation \|
	\|---\|---\|---:\|---:\|---\|
	\| [libero-all-suites/init_variants/core_mean](libero-all-suites/init_variants/core_mean) \| v0 init `core_mean` \| 4/4 \| 584,536,135 \| Training only; evaluation not run \|
	\| [libero-all-suites/init_variants/core16_21](libero-all-suites/init_variants/core16_21) \| v0 init `core16_21` \| 4/4 \| 584,536,135 \| Training only; evaluation not run \|
	\| [libero-all-suites/init_variants/no_video_pretraining](libero-all-suites/init_variants/no_video_pretraining) \| v0 init `no_video_pretraining` (no Wan video pretraining) \| 4/4 \| 584,536,135 \| Training only; evaluation not run \|
	\| [libero-all-suites/init_variants/core_random](libero-all-suites/init_variants/core_random) \| v0 init `core_random` \| 4/4 \| 584,536,135 \| 82.20% (4,932/6,000; 4 suites x seeds 42/43/44) \|
	\| [libero-all-suites/init_variants/ffn_magnitude](libero-all-suites/init_variants/ffn_magnitude) \| v0 init `ffn_magnitude` \| 4/4 \| 584,536,135 \| 92.12% (5,527/6,000; 4 suites x seeds 42/43/44) \|

	v0 (video 4 / action 4) retrained on all four suites with the same data, normalization, schedule and precision as `libero-all-suites/v0-video4-action4` (21,700 updates, global batch 128, seed 42) on two B200 GPUs; only the student initialization differs (see each directory's README and `init_variant_manifest.json`). Not evaluated. `init_variants/no_video_pretraining` starts without any Wan video pretraining and was trained on two H100s (2 GPUs x microbatch 8 x accumulation 8). `init_variants/core_random` was trained on two H100s (2 GPUs x microbatch 8 x accumulation 8) and scored 82.20% on the matched 6,000-episode four-suite evaluation. `init_variants/ffn_magnitude` was trained on two H100s (2 GPUs x microbatch 8 x accumulation 8) and scored 92.12% on the matched 6,000-episode four-suite evaluation.

	## October 26 addition: full-suite aligned 4/2

	\| Directory \| Model \| Video/action loops \| Parameters \| Evaluation \|
	\|---\|---\|---:\|---:\|---\|
	\| [libero-all-suites/v0-video4-action2](libero-all-suites/v0-video4-action2) \| v0 aligned \| 4/2 \| 584,536,135 \| Training only; evaluation not run \|

	Aligned (late) 4/2 v0 trained on all four suites with the same data, normalization, initialization recipe, schedule, precision and 2-GPU microbatch-8 layout as `libero-all-suites/v0-video4-action1` (21,700 updates, global batch 128, seed 42) on two H100s. Uses the LoopWAM_NT branch (revision `56e3328`). Not evaluated.

	## Checkpoint index

	\| Directory \| Model \| Video/action loops \| Parameters \| Final evaluation \|
	\|---\|---\|---\|---:\|---\|
	\| [libero-all-suites/init_variants/core_mean](libero-all-suites/init_variants/core_mean) \| v0 init `core_mean` \| 4/4 \| 584,536,135 \| Training only; evaluation not run \|
	\| [libero-all-suites/v0-video4-action2](libero-all-suites/v0-video4-action2) \| v0 aligned \| 4/2 \| 584,536,135 \| Training only; evaluation not run \|
	\| [libero-all-suites/init_variants/core16_21](libero-all-suites/init_variants/core16_21) \| v0 init `core16_21` \| 4/4 \| 584,536,135 \| Training only; evaluation not run \|
	\| [libero-all-suites/init_variants/no_video_pretraining](libero-all-suites/init_variants/no_video_pretraining) \| v0 init `no_video_pretraining` (no Wan video pretraining) \| 4/4 \| 584,536,135 \| Training only; evaluation not run \|
	\| [libero-all-suites/init_variants/core_random](libero-all-suites/init_variants/core_random) \| v0 init `core_random` \| 4/4 \| 584,536,135 \| 82.20% (4,932/6,000; 4 suites x seeds 42/43/44) \|
	\| [libero-all-suites/init_variants/ffn_magnitude](libero-all-suites/init_variants/ffn_magnitude) \| v0 init `ffn_magnitude` \| 4/4 \| 584,536,135 \| 92.12% (5,527/6,000; 4 suites x seeds 42/43/44) \|
	\| [libero-all-suites/dense-s30](libero-all-suites/dense-s30) \| dense_s30 \| 1/1 \| 1,416,114,247 \| Training only; evaluation not run \|
	\| [libero-all-suites/dense-s12](libero-all-suites/dense-s12) \| dense_s12 \| 1/1 \| 584,536,135 \| Training only; evaluation not run \|
	\| [libero-all-suites/v0-video1-action4](libero-all-suites/v0-video1-action4) \| v0 aligned \| 1/4 \| 584536135 \| Not evaluated (training only) \|
	\| [libero-long/v0-video4-action4-original](libero-long/v0-video4-action4-original) \| v0 \| 4/4 \| 584,536,135 \| 96/100 (96%) \|
	\| [libero-long/v1-video4-action4](libero-long/v1-video4-action4) \| v1 \| 4/4 \| 584,536,135 \| 82/100 (82%) \|
	\| [libero-long/dense-s12](libero-long/dense-s12) \| dense_s12 \| 1/1 \| 584,536,135 \| 81/100 (81%) \|
	\| [libero-long/v2-video4-action4](libero-long/v2-video4-action4) \| v2 \| 4/4 \| 584,536,135 \| 81/100 (81%) \|
	\| [libero-long/dense-s30](libero-long/dense-s30) \| dense_s30 \| 1/1 \| 1,416,114,247 \| 86/100 (86%) \|
	\| [libero-all-suites/v0-video4-action4](libero-all-suites/v0-video4-action4) \| v0 \| 4/4 \| 584,536,135 \| 388/400 (97%) \|
	\| [libero-long/v0-video4-action1](libero-long/v0-video4-action1) \| v0 \| 4/1 \| 584,536,135 \| 81/100 (81%) \|
	\| [libero-long/v0-video4-action2](libero-long/v0-video4-action2) \| v0 \| 4/2 \| 584,536,135 \| 86/100 (86%) \|
	\| [libero-long/v0-video2-action2](libero-long/v0-video2-action2) \| v0 \| 2/2 \| 584,536,135 \| 81/100 (81%) \|
	\| [libero-long/v0-video1-action4](libero-long/v0-video1-action4) \| v0 \| 1/4 \| 584,536,135 \| 86/100 (86%) \|
	\| [libero-long/v0-video4-action4-repeat](libero-long/v0-video4-action4-repeat) \| v0 \| 4/4 \| 584,536,135 \| 91/100 (91%) \|

	`libero-long/`: matched 344-training / 44-validation demonstration split, 7,250 updates. Dense-S12/S30 execute their independent blocks once; their 1/1 metadata does not mean the dense networks have equal depth. S12 has 12 independent block pairs; S30 has 30. Loop models use three prelude, six shared core and three coda pairs, with 3 + 6K + 3 effective block applications per expert.

	`libero-all-suites/`: one v0 trained on all 1,712 locally available Spatial/Object/Goal/Long demonstrations, 21,700 updates. Its 388/400 result aggregates all four suites, so it is a different training/evaluation scope from the Long-only rows. The same four-suite checkpoint also scored 720/2,000 (36%) on LIBERO-Pro.

	The original checkpoint-index rows below use 100 final-checkpoint rollouts at evaluation seed 42; the new KV table above reports three seeds. Standard-suite evaluations use 700 policy steps, 30 settling steps, 32-action predictions, replanning every ten actions, ten denoising steps and CFG 1. The original 4/4 checkpoint additionally scored 96%, 88%, 91% across evaluation seeds 42/43/44. These are one training seed and cannot establish across-training-seed significance.

	## Files and integrity

	Each directory contains `policy.pt`, `dataset_stats.json`, `data_manifest.json`, `training_manifest.json`, `training_timing.json`, `evaluation_summary.json`, and `export.json`. `policy.pt` is the native `loopwam-s-v1` checkpoint, containing FP32 model tensors, architecture/depth metadata and the training contract. Adam optimizer states are omitted; use it for inference or fresh-optimizer fine-tuning, not exact optimizer-state resume. All exported model tensors were checked for exact equality with the evaluated final checkpoint. `export.json` records both the original checkpoint SHA-256 and the exported file SHA-256; these hashes differ because optimizer removal changes serialization.

	The frozen VAE and text encoder/embeddings are external inference dependencies. The native loader does not require the donor initialization artifact when `checkpoint_path` is provided. Normalization files are specific to each checkpoint and must remain matched. Videos, dataset videos and original optimizer checkpoints remain on the training server.

	## Loading

	Use the compatible [LoopWAM code](https://github.com/anhdao69/LoopWAM_NT/tree/LoopWAM_NT), at revision `8a29ffdce1537409e2b7e975e838d990ebbe068a` or a compatible later revision. This is a custom policy, not a Transformers AutoModel checkpoint. Install that repository’s inference dependencies first. The repository is publicly readable.

	```python
	import torch
	from huggingface_hub import hf_hub_download
	from fastwam.models.wan22.loopwam import create_loopwam

	repo = "anhdao69/LoopWAM_NT"
	variant = "libero-long/v0-video4-action4-repeat"
	checkpoint = hf_hub_download(repo, f"{variant}/policy.pt")
	stats = hf_hub_download(repo, f"{variant}/dataset_stats.json")
	vae = hf_hub_download("Wan-AI/Wan2.1-T2V-1.3B", "Wan2.1_VAE.pth")
	model = create_loopwam(checkpoint_path=checkpoint, vae_path=vae,
	model_dtype=torch.float32, device="cuda").eval()
	```

	Depth/version are reconstructed from checkpoint metadata. Use the repository’s observation adapter, matched normalization, text embeddings with their padding masks, and BF16 autocast during prediction. See `scripts/evaluate_loopwam_libero.py` for the complete simulator pipeline. Evaluation summaries retain the original checkpoint hashes; use the tensor equality and source/export mapping in `export.json` when auditing exports. The evaluator computes the exported file’s own hash for a new evaluation.

	## Provenance and rights

	These research checkpoints derive from pretrained Wan and the FastWAM/LoopWAM implementation. Consult the upstream model and code licenses before redistribution or use; this model card does not grant additional upstream rights. The repository is currently public.

	For detailed comparison caveats, runtime accounting and per-task results, see the [consolidated report](https://github.com/anhdao69/LoopWAM_NT/blob/LoopWAM_NT/plans/performance/LoopWAM_Consolidated_Results_20261008.md). That report’s earlier snapshot labels the repeat pending; the repeat is now complete at 91/100, as recorded here.