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MATS-SQL Bundle — Progress Snapshot

Bundle: thanhdath/mats-sql-bundle (HuggingFace) Snapshot date: 2026-05-13

Original Task

Multi-agent Text2SQL pipeline reaching pass@8 ≥ 67% (BIRD-dev, EX). Hard constraints from project owner:

  • Planner ≤ 3B params
  • Selection ≤ 3B params
  • Validator(s) and Fixer ≤ 1B params each (prefer 0.5B)
  • Max ORPO iter-2 (no iter-3+)
  • No commercial APIs (no GPT teacher) — collaborative training must be structurally necessary
  • V+F (validators + fixer) must not hurt final accuracy and should contribute to acc increase
  • Results tables must include per-agent parameter counts
  • Two specialized validators per paper §Combined Validator: v_s (selection) and v_c (condition)

Current Progress (latest)

Best result so far (BIRD-dev, K=8)

Config N pass@8 (recall, oracle) Trained selector (real, no leak) Notes
1-stage iter-2 uniform-temp 1524 64.96% — leak-pre-patch number was 64.96
1-stage iter-2 mixed-temp (0.5/0.7/0.9/1.1) 1525 65.38% — best recall
3-stage iter-2 + 0.6B v_s/v_c + ORPO fixer iter-1 1525 65.05% — V+F neutral (0 rescues)
3-stage iter-2 + 0.5B v_s + 0.6B v_c + 0.5B replanner-fixer iter-2 1525 65.11% 54.30% leak-free selector measurement; fixer iter-2 +0.06pp vs iter-1

Critical finding (2026-05-13): compute_bestofn_with_selector.py had exec_response = "OK" if is_planner_correct else "Error / no rows" — passed the gold-graded label to the selector → trivially matched oracle. Patched to use actual SQL execution result. Real trained-selector accuracy is 54.30%, NOT 65%. Selector → oracle gap = 10.81pp.

Two headline gaps to close (target = headline EX ≥ 67%)

Gap Current Target Gap Lever
Oracle pass@8 (recall) 65.38% (mixed-temp) ≥70% +4.6pp More diverse sampling / better fixer rescues
Selector → oracle 57.25 / 65.38 = 87.6% ≥95% +8.13pp Selector v3 (more data / different arch)

Selector v2 results (2026-05-13 — row preview added to prompt)

Retrained 3B selector with OK. Rows preview: <rows> instead of bare OK. Closes ~3-4pp of the ~11pp gap.

Config (BIRD-dev K=8 iter-2 planner) Oracle Selector v1 (no rows) Selector v2 (rows) v2 gain
1-stage uniform-temp 64.96% 53.94% 56.43% +2.49pp
1-stage mixed-temp 65.38% 53.64% 57.25% ← headline +3.61pp
2-stage + ORPO fixer-iter1 63.04% 52.72% 56.02% +3.30pp
3-stage + 0.6B V/V + ORPO fixer-iter1 65.05% 53.11% 56.98% +3.87pp
3-stage + 0.5B V/V + 0.5B replanner-fixer 65.11% 53.51% 56.66% +3.15pp

Headline pass@8 post-selector = 57.25% (1-stage mixed-temp + v2 selector). Still −9.75pp from 67%.

Per-model status

Agent Model Params Status
Planner Qwen2.5-Coder-3B-Instruct + ORPO iter-2 (collab) 3B trained ✓
Selector Qwen2.5-Coder-3B-Instruct + SFT (YES/NO binary) 3B trained ✓
Validator-Selection (v_s) Qwen2.5-Coder-0.5B-Instruct + SFT v3 0.5B trained ✓
Validator-Condition (v_c) Qwen3-0.6B-Instruct + SFT v3 0.6B trained ✓ (0.5B variant truncated, retrain pending)
Fixer (re-planner) Qwen2.5-Coder-0.5B + ORPO iter-2 (replanner data) 0.5B trained ✓

V+F contribution diagnostic (mixed-temp iter-2 K=8, 1525 q)

  • Validators critique 91.6% of trajectories; old fixer ignored 98.6% → 0 rescues at pass@8
  • Replanner fixer (iter-2) currently flipping ~40% of trajectories (winloss=605 at 1360/1534) — net effect being measured

Key findings

  • ORPO planner: SFT 64.11% → iter-1 64.26% → iter-2 64.96% (+0.70pp, diminishing returns)
  • Mixed-temp sampling adds +0.42pp over uniform-temp at K=8
  • v3 validator data rebalanced from 8% all-OK (over-critique) to 34.5%/61% all-OK
  • Fixer dataset re-built as "re-planner" objective (1833 pairs): given a failed planner trajectory, produce a correct alternative from same question's K=4 trajectories
  • pass@8 (true recall) is the upper bound for trained-selector accuracy; need recall ≥70% to land headline ≥67% after selector picks

What's Next (in priority order)

  1. Phase4 K=8 3-stage eval finishes (currently 89% done, ETA 30 min) → get pass@8 with replanner-fixer.
  2. Apply patched (leak-free) selector to all K=8 JSONLs → get true selector accuracy (not oracle).
  3. If pass@8 selector ≥67%: DONE. Write results table with per-agent sizes.
  4. If pass@8 selector <67%:
    • Re-mine fixer ORPO data on iter-2 planner BIRD-train rollouts (current data was from iter-1 planner → distribution shift).
    • Re-train fixer ORPO iter-2 on fresh data.
    • Consider planner sampling tricks: wider mixed-temp (0.3-1.3), nucleus variation.
  5. Make V+F contribute (currently neutral): the 0.5B replanner fixer's winloss is now high (~40%) — need to check if those flips are net+ or net-. If net-, gate fixer to only run on planner_exec_ok=False cases.

Repo contents 

mats-sql-bundle/
├── PROGRESS.md                                                  # this file
├── models/
│   ├── planner-iter2-collab-3B/                                 # 3B ORPO iter-2 planner
│   ├── selector-3B-sft/                                         # 3B trained selector
│   ├── validator-selection-0.5B-v3/                             # v_s SFT
│   ├── validator-condition-0.6B-v3/                             # v_c SFT
│   └── fixer-replanner-0.5B-iter2-orpo/                         # ORPO iter-2 re-planner fixer
├── data/
│   ├── sft-validator-selection-v3/                              # SFT data for v_s
│   ├── sft-validator-condition-v3/                              # SFT data for v_c
│   ├── hf_fixer_replanner/                                      # ORPO data for fixer iter-2
│   └── hf_planner_collaborative_iter2/                          # ORPO data for planner iter-2
├── recipes/
│   ├── orpo-planner-collab-iter2.yaml
│   ├── orpo-fixer-replanner-0.5b-iter2.yaml
│   ├── validator-selection-fft-0.5b-v3.yaml
│   └── validator-condition-fft-0.5b-v3.yaml
└── scripts/
    ├── run_pipeline_rollouts.py                                 # rollout / 3-stage runner
    ├── compute_bestofn_with_selector.py                         # selector apply (patched)
    ├── compute_bestofn_metrics.py                               # oracle/greedy metrics
    ├── build_validator_2agents_v3.py                            # v_s/v_c data builder
    └── build_fixer_replanner_iter2.py                           # fixer re-planner data builder

How to continue from this bundle 

# 1. Clone the bundle (~16 GB)
git clone https://huggingface.co/datasets/thanhdath/mats-sql-bundle
cd mats-sql-bundle

# 2. Symlink to alignment-handbook layout
ln -s $(pwd)/models /path/to/alignment-handbook/output
ln -s $(pwd)/data /path/to/mats-sql-tist/data/llm_alignment_imported

# 3. Continue from where we left off: BIRD-dev K=8 3-stage with iter-2 planner + 0.5B agents
bash scripts/run_pipeline_rollouts.py --K 8 --mixed_temp "0.5,0.7,0.9,1.1" ...

# 4. To do iter-3 (if needed), re-mine fixer data on iter-2 planner BIRD-train rollouts first.

Maintainer: thanhdath@gmail.com / thanhdath97@gmail.com Source repo: /home/datht/mats-sql-tist (private dev machine)