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docs: finalize phase1 report and launch phase2 attnres suite

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experiment_suites/2026-04-04-imf-horizon-grid/final_report.md Normal file
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# Phase-1 Final Report and Phase-2 Handoff
- Finalized: 2026-04-05 00:34:20 CST
- Scope: IMF AttnRes policy horizon/action-step grid on `sim_transfer`
- Fixed setup: `n_emb=384`, `n_layer=12`, batch size `80`, learning rate `2.5e-4`, `max_steps=50k`, rollout every 5 epochs with 5 episodes, 3 cameras `[r_vis, top, front]`.
- Main metric: `checkpoints/vla_model_best.pt` 中记录的训练期最大 `rollout_avg_reward`。
## Final leaderboard
| Rank | Run ID | pred_horizon | executed action steps | Best avg_reward | Best step | Final loss |
|---:|---|---:|---:|---:|---:|---:|
| 1 | `ph16_ex8` | 16 | 8 | **610.8** | 21874 | 0.0034 |
| 2 | `ph16_ex16` | 16 | 16 | 561.2 | 48124 | 0.0045 |
| 3 | `ph32_ex32` | 32 | 32 | 513.2 | 43749 | 0.0040 |
| 4 | `ph8_ex8` | 8 | 8 | 415.6 | 48124 | 0.0070 |
| 5 | `ph32_ex8` | 32 | 8 | 361.6 | 43749 | 0.0048 |
| 6 | `ph32_ex16` | 32 | 16 | 239.6 | 48124 | 0.0038 |
## Final conclusions
1. **最佳组合是 `pred_horizon=16` + `num_action_steps=8`**，最佳平均奖励为 **610.8**，出现在 **step 21874**。
2. 在 `pred_horizon=16` 下，执行 8 步优于执行 16 步，优势约 **+8.8%**（610.8 vs 561.2）。
3. `pred_horizon=32` 时，对执行步长非常敏感：`32/32` 明显优于 `32/8` 和 `32/16`；特别是 `32/16` 退化最明显。
4. 更长的预测窗口并不会自动带来更高 reward；**预测窗口与实际执行窗口的匹配关系** 是关键。
5. 最佳 checkpoint 并不在训练结束时出现，而是在 50k 训练中较早的 **21.9k step** 出现，说明 rollout 验证比仅看 train loss 更重要。
6. 因而 Phase-2 的比较基线固定为 **`ph16_ex8`**。
## Recommended baseline for follow-up experiments
- Baseline run: `ph16_ex8`
- Baseline best checkpoint: `step 21874`
- Baseline best avg_reward: `610.8`
- Baseline run dir: `/home/droid/roboimi_suite_20260404/runs/imf-p1-ph16-ex08-emb384-l12-ms50k-5880g1-20260404-131223`
## Phase-2 target: full-AttnRes vision backbone
本阶段按你的要求，不再只是 IMF head 中使用 AttnRes，而是把**之前视觉 ResNet 主干中的残差单元全部替换为 AttnRes 残差单元**。当前实现保留了 ResNet 风格的 stage / downsample 宏观结构，但视觉残差 trunk 已切换到 AttnRes：
- implementation: `roboimi/vla/models/backbones/attnres_resnet2d.py`
- wiring: `roboimi/vla/models/backbones/resnet_diffusion.py`
- config: `roboimi/vla/conf/backbone/resnet_diffusion.yaml`
相关代码已提交：
- `a780068` — headless rollout 修复 + Phase-1 汇总
- `2033169` — full-AttnRes vision backbone
## Phase-2 launch status (observed on 2026-04-05 00:36 CST)
- Run: `imf-p2-full-attnres-vision-ph16-ex08-emb384-l12-b40-lr1p25e4-ms50k-l20g3-20260405-002424`
- Host: `100.119.99.14`, GPU `3`
- Config anchor: `pred_horizon=16`, `num_action_steps=8`
- Vision backbone: `attnres_resnet`
- Because batch size `80` OOMed on both local 5090 and remote L20, Phase-2 currently uses:
- batch size: `40`
- learning rate: `1.25e-4`
- Latest confirmed progress: **step 1300**
- First rollout has **not happened yet** at this observation point.
- SwanLab: https://swanlab.cn/@game-loader/roboimi-vla/runs/xy7fjdmn0stdr19eu3gub
## Next action
继续监控 Phase-2 full-AttnRes 训练，待其完成后直接与 Phase-1 baseline `610.8` 做对比，判断“视觉主干全部替换为 AttnRes”是否优于“仅 IMF 中使用 AttnRes”。

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experiment_suites/2026-04-04-imf-horizon-grid/status.json
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experiment_suites/2026-04-05-full-attnres-vision-phase2/manifest.json Normal file
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experiment_suites/2026-04-05-full-attnres-vision-phase2/notes.md Normal file
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# Full-AttnRes Vision Phase-2
- Created: 2026-04-05 00:12:14
- Baseline reference: ph16_ex8 best avg_reward=610.8
- Candidate run: imf-p2-full-attnres-vision-ph16-ex08-emb384-l12-ms50k-20260405-001214
- 2026-04-05 00:23:03: batch=80 OOM on both 5090 and L20; using validated fallback batch=40, lr=1.25e-4 on remote L20 GPU3.
- 2026-04-05 00:24:24: launching candidate imf-p2-full-attnres-vision-ph16-ex08-emb384-l12-b40-lr1p25e4-ms50k-l20g3-20260405-002424 on 100.119.99.14 GPU3 with batch=40 lr=1.25e-4.
- 2026-04-05 00:27:17: remote phase2 run is active on 100.119.99.14 GPU3, validated at least to step 200. SwanLab: https://swanlab.cn/@game-loader/roboimi-vla/runs/xy7fjdmn0stdr19eu3gub
- 2026-04-05 00:36:54: latest confirmed progress is step 1300 on 100.119.99.14 GPU3; first rollout not reached yet.

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experiment_suites/2026-04-05-full-attnres-vision-phase2/status.json Normal file
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