Add Blockwise-OAT baseline artifacts (README.md)

README.md

@@ -1,3 +1,133 @@
 ---
 license: mit
+tags:
+- robotics
+- manipulation
+- oat
+- libero
+- blockwise-decoding
 ---
+
+# Blockwise-OAT — strict original-OAT baseline (LIBERO-10)
+
+Paired evaluation of **autoregressive (AR)** vs **blockwise parallel tail** action-token
+generation on a frozen [OAT](https://arxiv.org/abs/2602.04215) policy.
+
+**HF repo:** [hackhackhack66666/Blockwise-OAT](https://huggingface.co/hackhackhack66666/Blockwise-OAT)  
+**Code branch:** `Blockwise-OAT` on [GadzhiAskhabaliev/OAT-BLT-Dense](https://github.com/GadzhiAskhabaliev/OAT-BLT-Dense)
+
+## Summary
+
+| Metric | AR baseline | Blockwise (P=4, r=1) |
+|--------|-------------|----------------------|
+| LIBERO-10 mean SR | **58.73% ± 0.18%** | **52.33% ± 1.04%** |
+| Δ vs AR | — | **-6.40 pp** |
+| Decoder speedup (bs=1) | — | **1.16×** |
+| E2E `predict_action` (bs=1) | 36.4 ms | 30.1 ms (**1.21×**) |
+| Tail train epochs | — | 15 (final CE 3.0607) |
+
+AR baseline exceeds the published OAT8 reference (~56.3%) on our cluster stack.
+Blockwise achieves **>1× decoder speedup** but **−6.4 pp** SR at 15 tail epochs (resume training planned).
+
+## Baseline artifacts (frozen)
+
+| Component | Source |
+|-----------|--------|
+| Policy | [Mirageinv/oat — policy_ep-0250_sr-0.596.ckpt](https://huggingface.co/Mirageinv/oat) |
+| Tokenizer | [Mirageinv/oat — tokenizer_ep-0950_mse-0.002.ckpt](https://huggingface.co/Mirageinv/oat) |
+| Tail decoder | `checkpoints/original_oat_tail_p4_r1.pt` (this repo) |
+
+## Architecture & data flow
+
+OAT encodes observations and generates **8 action tokens** `z₁…z₈`. Blockwise-OAT splits decoding:
+
+```
+Obs (RGB + proprio) ──► Vision encoder ──► cond [B, T_o, d]
+                                            │
+            ┌───────────────────────────────┴───────────────────────────────┐
+            │ AR path (baseline)                                          │
+            │   BOS ──► AutoregressiveModel.generate (8 steps) ──► z₁…z₈ │
+            └───────────────────────────────┬───────────────────────────────┘
+                                            │
+            ┌───────────────────────────────┴───────────────────────────────┐
+            │ Blockwise path                                                │
+            │   BOS ──► generate_prefix (P=4 AR steps) ──► z₁…z₄, h_prefix │
+            │   (z₁…z₄, h_prefix) ──► ParallelTailDecoder (1 pass) ──► z₅…z₈│
+            └───────────────────────────────┬───────────────────────────────┘
+                                            ▼
+                      cat(z_prefix, z_tail) ──► OATTok.detokenize ──► action chunk
+```
+
+**Inputs:** multi-view RGB, robot state, task id (same as OAT).  
+**Outputs:** `action` / `action_pred` tensors (identical shapes for AR and Blockwise).  
+**Trainable in this run:** only `ParallelTailDecoder` (~4.5M params, 0.90× AR size).
+
+### Generation schedule
+
+| Mode | AR forward passes | Tail passes |
+|------|-------------------|-------------|
+| Full AR | 8 | 0 |
+| Blockwise P=4 | 4 | 1 |
+
+## Experiment protocol
+
+1. Download Mirageinv/oat policy + tokenizer.
+2. Train `ParallelTailDecoder` on `libero10_N500` with frozen policy (15 epochs, bs=64, lr=1e-4).
+3. Paired sim-eval: `50` episodes/task × `3` seeds (`test_start_seed=1000`).
+4. Benchmarks: dataset / training / policy verification + wall-clock speed.
+
+Cluster launcher: `scripts/cluster/run_blockwise_original_oat_baseline.sh` (`PHASE=B NUM_EXP=3`).
+
+## Visualizations
+
+| Figure | Description |
+|--------|-------------|
+| ![AR eval](benchmarks/ar_sim_eval_dashboard.png) | AR per-task SR |
+| ![Blockwise eval](benchmarks/blockwise_sim_eval_dashboard.png) | Blockwise per-task SR |
+| ![Paired](benchmarks/paired_sr_comparison_dashboard.png) | Side-by-side per-task comparison |
+| ![Speed](benchmarks/speed_benchmark_dashboard.png) | Decoder + E2E latency |
+| ![Tail train](benchmarks/tail_training_dashboard.png) | Tail CE loss curve |
+| ![Verify](benchmarks/verification_summary_dashboard.png) | Verification kit |
+
+## Repository layout
+
+```
+checkpoints/original_oat_tail_p4_r1.pt   # trained tail decoder
+eval/ar_eval_log.json                    # AR sim metrics
+eval/blockwise_eval_log.json             # Blockwise sim metrics
+benchmarks/*.json                        # verification + speed raw logs
+benchmarks/*_dashboard.png               # plots above
+```
+
+## Reproduce inference
+
+```bash
+python scripts/eval_policy_sim.py \
+  -c output/baselines/original_oat/hf/policy_ep-0250_sr-0.596.ckpt \
+  -o output/eval/blockwise/ar \
+  --tokenizer-checkpoint output/baselines/original_oat/hf/tokenizer_ep-0950_mse-0.002.ckpt
+
+python scripts/eval_policy_sim.py \
+  -c output/baselines/original_oat/hf/policy_ep-0250_sr-0.596.ckpt \
+  -o output/eval/blockwise/bw \
+  --use-blockwise --blockwise-prefix-len 4 --blockwise-refine-iters 1 \
+  --blockwise-tail-checkpoint checkpoints/original_oat_tail_p4_r1.pt \
+  --tokenizer-checkpoint output/baselines/original_oat/hf/tokenizer_ep-0950_mse-0.002.ckpt
+```
+
+## Citation
+
+```bibtex
+@misc{liu2026oatorderedactiontokenization,
+      title={OAT: Ordered Action Tokenization},
+      author={Chaoqi Liu and Xiaoshen Han and Jiawei Gao and Yue Zhao and Haonan Chen and Yilun Du},
+      year={2026},
+      eprint={2602.04215},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO}}
+```
+
+## Next steps
+
+- Resume tail training (target 30+ epochs) and re-run paired eval.
+- Ablate `refine_iters`, prefix length `P`, and learning-rate schedule.