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exp190 phase-balanced deep MultiPV soft targets (SF18)
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Progress report — 2026-07-10 / 2026-07-11

Session goal: push the latest 200M compact-vocab ChessTransformer toward high Elo without MCTS (pure next-move / policy argmax), while growing better soft-target data.

Summary

Item Result
Base model avewright/chess-transformer-200m-compact-soft
Finetune run exp189 — soft MultiPV + hard deep labels + h-flip
Deep data harvest exp190 — phase-balanced SF18 MultiPV (ongoing)
Checkpoint pushed latest step 2851, best by holdout metric at step 500
Pure-policy Elo (vs SF18 limited) ~1700 (bracket 1500–1800)
Training status Stopped on request after plateau

What we shipped (code)

Path Role
experiments/exp189_200m_maxelo_policy.py Max-Elo policy finetune; mixes shallow soft + deep soft + hard HF
experiments/exp190_phase_deep_harvest.py Phase-quota MultiPV harvest (SF18, Syzygy, multi-source FENs)
scripts/run_exp189_200m_maxelo.sh Initial exp189 launch
scripts/run_exp189_deep_mix.sh Resume with --deep-soft-cache mix
scripts/run_exp190_phase_deep.sh Harvest launcher (40 workers, SF18 vnni512)
play_factory_gui.py Fix: Chess960 castling UCI (e8a8) → standard (e8c8) for chess.js

Training recipe (exp189)

Resumed from compact-soft, then continued with deep-mix:

  • Soft frac 0.72, soft α 0.40, deep-mix frac 0.40 among soft steps
  • Batch 448 × 2 accum, LR 4e-6 cosine, warmup 200
  • H-flip aug p=0.5 on soft batches
  • Hard ballast: HF lichess-sf stream, min_depth≥15
  • Eval: shallow holdout + deep holdout; best.pt tracks blended top-1

Observed learning

Deep-mix run plateaued around:

  • Shallow soft holdout top-1 ≈ 38%
  • Deep soft holdout top-1 ≈ 41%
  • Blended best metric 0.3966 recorded at step 500 of the deep-mix resume
  • Shutdown save: latest.pt at step 2851

Losses stayed flat (soft 2.38, hard CE ~1.79) — model had largely extracted the signal from the then-available mix (2M shallow + ~96–175k deep).

Soft targets — why they matter

Hard labels = one Stockfish best move. Soft MultiPV labels = a distribution over plausible moves (score → softmax). That teaches:

  1. Near-equal alternatives (not overconfident on one line)
  2. Better calibration under legal-mask argmax (no search)
  3. Deeper MultiPV (exp190) = sharper teacher in middlegame/endgame

exp186 already provided ~2M shallow soft rows (avewright/exp186-sf-multipv-2m). exp190 adds phase-balanced deeper labels so training does not drown in openings.

exp190 harvest design

Targets: 22% opening / 48% middlegame / 30% endgame

Lever Design
Depth-by-phase opening 10–14, mid 12–16, endgame 14–18
Teacher Stockfish 18 full strength (stockfish/stockfish-latest vnni512)
Sources HF streams + book playouts + endgame templates + random walks
Quotas Deficit-first producer + hard gate at writer
Tags phase, label_depth in soft_cache.pt for stratified training
Syzygy Workers probe tablebases when present

At upload time harvest was ~187k written positions (target 1M, still running), phase mix roughly on quota. See outputs/exp190_phase_deep/ARCHITECTURE.md.

Elo evaluation

Script: elo_eval_latest.py
Checkpoint: outputs/exp189_200m_maxelo_policy/latest.pt
Opponent: SF18 UCI_LimitStrength, 50ms/move, 8 openings × both colors, Syzygy + book.

SF Elo Score W–D–L
1500 0.625 7–6–3
1800 0.438 4–6–6

Estimated Elo ≈ 1700 (noisy; ±~150 with this sample).
Weakness: Black side (more losses / short mates). No MCTS at inference.

Hugging Face artifacts

Repo Type Contents
avewright/chess-transformer-200m-maxelo model best_model.pt, latest_model.pt, config, Elo + progress docs
avewright/exp190-phase-deep-soft dataset soft_cache.pt, jsonl shards, status, architecture notes

Base still: avewright/chess-transformer-200m-compact-soft
Shallow soft still: avewright/exp186-sf-multipv-2m

How to load for inference 

export MOVE_VOCAB_VERSION=compact
python play_factory_gui.py --checkpoint path/to/latest_model.pt
# or
python elo_eval_latest.py path/to/latest_model.pt

Policy-only: legal-mask argmax on policy_logits (see elo_eval_latest.get_model_move_generic).

Next levers (not done this session)

  1. Grow exp190 to 1M+ and retrain with stratified phase sampling + depth weights
  2. Longer Elo gauntlet (more games / level) and evaluate best vs latest
  3. AlphaZero-style expert iteration (exp183_selfplay.py / rl_selfplay/) after supervised plateau
  4. Fix Black-side weakness (phase-balanced Black STM oversampling, or STM-normalized training audit)

Ops notes

  • Training stopped cleanly: Saved on shutdown at step 2851
  • Harvest left running on CPU (40 workers) so it does not starve a future GPU train
  • .env / tokens are gitignored — never commit HF or GitHub secrets