| --- |
| license: mit |
| pretty_name: exp190 Phase-Balanced Deep Soft Targets |
| tags: |
| - chess |
| - stockfish |
| - multipv |
| - soft-labels |
| - imitation-learning |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # exp190 — Phase-balanced deep MultiPV soft targets |
|
|
| Stockfish **18** MultiPV soft-policy labels with **phase quotas** so training |
| does not drown in openings. Built for policy-only (no-MCTS) ChessTransformer |
| finetunes such as [`avewright/chess-transformer-200m-maxelo`](https://huggingface.co/avewright/chess-transformer-200m-maxelo). |
|
|
| ## Motivation |
|
|
| Naive HF streaming + shallow MultiPV skews toward openings. Elo without search is |
| decided in middlegame/endgame. This harvest enforces: |
|
|
| | Phase | Target share | Label depth | |
| |-------|--------------|-------------| |
| | Opening | 22% | 10–14 | |
| | Middlegame | 48% | 12–16 | |
| | Endgame | 30% | 14–18 | |
|
|
| ## Contents |
|
|
| | Path | Description | |
| |------|-------------| |
| | `soft_cache.pt` | Torch cache ready for exp189-style training | |
| | `data/*.jsonl` | Raw labeled positions (FEN + MultiPV soft targets) | |
| | `status.json` | Harvest counters at upload time | |
| | `ARCHITECTURE.md` | Design notes for stratified training | |
|
|
| ### `soft_cache.pt` tensors |
| |
| `board_array`, `turn`, `castling`, `ep_square`, `move_idx`, `cp`, `mate`, |
| `soft_indices`, `soft_probs`, **`phase`**, **`label_depth`**. |
| |
| Phase encoding: `0=opening`, `1=middlegame`, `2=endgame`. |
| |
| ## How it was generated |
| |
| - Engine: Stockfish 18 (`x86-64-vnni512`), full strength, MultiPV 8 |
| - FEN sources: HF position streams, book playouts, endgame templates, random walks |
| - Deficit-first producer + hard phase gate at writer |
| - Syzygy tablebases when available |
| - Code: `experiments/exp190_phase_deep_harvest.py` in [avewright/transform](https://github.com/avewright/transform) |
| |
| ## Recommended training use |
| |
| 1. Mix with shallow volume (`exp186-sf-multipv-2m`) at ~30–40% deep / 60–70% shallow |
| 2. Stratify batches by `phase` (1/3 each) so gradients never bog on openings |
| 3. Optional depth weight: `w = label_depth / mean_depth` (clip 0.5–1.5) |
| |
| ## Related |
| |
| - Shallow soft: [`avewright/exp186-sf-multipv-2m`](https://huggingface.co/datasets/avewright/exp186-sf-multipv-2m) |
| - Model trained with this mix: [`avewright/chess-transformer-200m-maxelo`](https://huggingface.co/avewright/chess-transformer-200m-maxelo) |
| - Progress write-up: `docs/PROGRESS_2026-07-10.md` in the transform repo |
| |
