# gamevla — CS2 VLA / behavioral-cloning checkpoints

Checkpoints for real-environment testing. One folder per experiment; each folder has
`config.yaml`, `dataset_statistics.json`, the selected checkpoint(s), and an `EVAL.md` with the
exact run command. For experiments with more than one saved checkpoint we keep the **final** step
and one **intermediate** step; single-checkpoint experiments keep the only one.

**▶ To run these in the CS2 environment, see [EVALUATION.md](EVALUATION.md)** (score = mean total
episode reward) and each folder's `EVAL.md`.

`aim_cos` = eval mouse-aim cosine similarity (the aim quality metric; ~0 means aim did not
learn). `key_f1` = eval h0 key macro-F1. `attack_f1` = eval firing F1.

| folder | checkpoint | step | aim_cos | key_f1 | attack_f1 |
|---|---|---|---|---|---|
| `csbc` | `steps_5000.pt` (best eval) | 5000 | 0.00 | **0.285** | 0.156 |
| `csbc` | `steps_15000.pt` (final)   | 15000 | 0.00 | 0.265 | 0.179 |
| `nitrogen_starvla` | `steps_30000.pt` (mid) | 30000 | 0.00 | 0.179 | 0.058 |
| `nitrogen_starvla` | `final_model.pt` (final, complete 60k) | 60000 | 0.01 | 0.191 | 0.065 |
| `aimflow` | `steps_5000.pt` (only) | 5000 | 0.475 ⚠️ | 0.143 | 0.042 |
| `aimflow_v2` | `steps_10000.pt` (mid) | 10000 | -0.01 | 0.151 | 0.027 |
| `aimflow_v2` | `steps_15000.pt` (final) | 15000 | 0.01 | 0.158 | 0.021 |
| `aimflow_v3` | `steps_30000.pt` (mid) | 30000 | -0.03 | 0.182 | 0.117 |
| `aimflow_v3` | `steps_55000.pt` (final) | 55000 | 0.04 | 0.157 | 0.025 |

Notes:
- ⚠️ **`aimflow` (v1) aim_cos ≈ 0.475 is a metric artifact, not real aim**: v1 used a different mouse
  normalization, so its aim_cos is NOT comparable to v2/v3 (which read ~0). Across all experiments here,
  aim is not convincingly learned — treat it as an open question to verify in the real environment.
- `csbc` peaks early (step 5000) then overfits on keys; `steps_15000` is more trained but slightly worse.
- `nitrogen_starvla/final_model.pt` is the only fully-completed (60k-step) run.