exp190-phase-deep-soft / ARCHITECTURE.md
avewright's picture
exp190 phase-balanced deep MultiPV soft targets (SF18)
975ec71 verified
|
Raw
History Blame Contribute Delete
2.15 kB
# exp190 — saturating soft data without starving training
## Problem
exp186 soft cache was ~56% opening / 22% mid / 22% end. Openings label
faster (shallower trees) so naive HF streaming floods the dataset. Training
then overfits early-game priors and underperforms in middlegame/endgame —
exactly where Elo is decided without MCTS.
## Data architecture
| Lever | Design |
|---|---|
| Phase quotas | 22% opening / 48% middlegame / 30% endgame (hard gate at writer) |
| Depth-by-phase | op 10–14, mid 12–16, eg 14–18 (full-strength SF18, no Elo limit) |
| Multi-source FENs | HF stream + book playouts + endgame templates + random walks |
| Deficit-first feed | Producer oversamples the lagging phase every cycle |
| Syzygy | Wired into each worker when `syzygy/*.rtbw` present |
| soft_cache tags | `phase` + `label_depth` tensors for stratified training |
## Training efficiency (how to consume this)
1. **Stratified batches** — sample 1/3 from each phase bucket every step so
gradients never bog on openings.
2. **Depth weights**`w = label_depth / mean_depth` (or clip 0.5–1.5) so
deeper MultiPV pulls harder than shallow noise.
3. **Critical upweight** — boost high `cp_gap_top1_top2` / low entropy rows
(forcing moves teach more than quiet equals).
4. **Merge, don't replace** — mix exp190 deep cache with exp186 2M shallow
at ~30/70 so you keep volume + add quality.
5. **Round-robin curriculum** — alternate soft-deep / soft-shallow / hard-HF
micro-batches inside the same step (already partially done via soft_frac).
## Model-side advantages (no MCTS)
- Phase-balanced policy → fewer “opening genius / endgame idiot” failures
- Deeper soft targets → better next-move calibration under legal-mask argmax
- Endgame + Syzygy teacher → converts tablebase-perfect moves into policy mass
- Compact vocab + spatial head already amortizes move representation
## Ops
- SF binary: `stockfish/stockfish-latest` → SF18 `x86-64-vnni512` (Xeon 6342)
- 64 workers × 96MB hash ≈ 6GB; ~441GB RAM free on this box
- Safe alongside GPU exp189 (CPU-only)
- Tail: `tail -f outputs/exp190_phase_deep/run.log`