| name | source-command-megaminx-rescue |
| description | Run the top-N long-pid sym-ensemble rescue + merge cycle on the current best megaminx submission. |
source-command-megaminx-rescue
Use this skill when the user asks to run the migrated source command megaminx-rescue.
Command Template
/megaminx-rescue
Automate the canonical "rescue the long-tail" cycle that we run after each new
best submission. Extracts the top-N longest paths from the current best CSV,
runs sym-ensemble + qshort + beam 524k on those pids, merges the rescued
paths into the base, verifies the merged CSV, and prepares it for submission.
Usage
/megaminx-rescue 50 — rescue top 50 long-pids of current best submission with K=4 sym (default)./megaminx-rescue 80 K=8 — rescue top 80 with K=8 (diminishing returns past K=4)./megaminx-rescue 50 base=path/to/sub.csv K=4 — explicit base CSV.
Defaults
- Base submission: latest in
megaminx/submissions/ matching merge_*.csv (highest path count = lowest score).
- K (sym-ensemble): 4.
- Beam: 524288.
- Max-steps: 150.
- Teacher:
megaminx/models/m05_bellman_warm/epoch_0499.pt.
- Student (qshort):
megaminx/models/m23_v2_sym_aware/epoch_0499.pt (sym-aware, pair with --sym-ensemble).
- Output prefix:
merge_plus_sym{K}_top{N}.
- Rotations:
megaminx/data/rotations.npy (default; auto-loaded by --sym-ensemble).
Execution
1. Identify base submission and extract top-N pids
PYTHONUTF8=1 .venv/Scripts/python.exe -c "
import csv
sub = '<base>'
rows = list(csv.DictReader(open(sub)))
rows.sort(key=lambda r: -len(r['path'].split('.')))
print(','.join(r['initial_state_id'] for r in rows[:<N>]))
"
Report the pid list, length range of top-N, and sum of path lengths.
2. Launch the rescue solve (background)
nohup .venv/Scripts/python.exe megaminx/scripts/03_solve.py \
--checkpoint megaminx/models/m05_bellman_warm/epoch_0499.pt \
--qshort-student megaminx/models/m23_v2_sym_aware/epoch_0499.pt \
--qshort-alpha 2 \
--out megaminx/submissions/<output>.csv \
--pids <pid_list> \
--beams 524288 --max-steps 150 --bf16 \
--sym-ensemble <K> \
> megaminx/submissions/<output>.log 2>&1 &
3. Monitor with tight filter
tail -F megaminx/submissions/<output>.log | grep -E --line-buffered \
"attempts=20/|attempts=40/|attempts=60/|attempts=80/|Traceback|Error|verify:|stats:|wrote |OOM"
(Use Monitor tool with persistent: true.)
4. After completion: merge
.venv/Scripts/python.exe megaminx/scripts/16_merge_rescue.py \
--base <base>.csv \
--rescue megaminx/submissions/<output>.csv \
--out megaminx/submissions/<merged>.csv
Report: replaced X/N (saved Y moves), new total, regressions count (must be 0).
5. Verify merged
The merge script verifies inline and prints verify: 1001/1001 valid, total <T>.
If anything other than 1001/1001 valid: STOP, do not proceed to submit.
6. Tell user the result
Print: "ready to submit .csv (current_best - delta = new_total)".
Suggest /megaminx-submit <merged>.csv "<description>" as next step.
DO NOT submit automatically. The user controls Kaggle slot usage.
Gotchas
- Pair m23_v2 (sym-aware) with
--sym-ensemble, not the original m23 (distilled
on forward-state V; lower recall on inverted/rotated states). See
megaminx_gotchas.md "Q-shortlister student must match teacher" entry.
- K=4 is the sweet spot. K=8 saves ~80% as much per pid at 2× wall.
- Beam max-steps = 150 minimum (real path tail is ~99-103 in current submissions).
Lower max-steps causes silent failures (model never converges).
- The merge takes per-pid min of base vs rescue. If rescue is uniformly worse than
base for a pid, base is kept. 0 regressions is the expected outcome with K≥2.
- Rotation index 0 (identity) is always one of the K rotations selected.
Cost reference (4090 Laptop, beam 524k)
| K | wall/pid | 50 pids | 80 pids |
|---|
| 2 | ~140s | ~2h | ~3h |
| 4 | ~280s | ~4h | ~6h |
| 8 | ~560s | ~8h | ~12h |
Pick K based on remaining session time and previous-pass exhaustion.
