Datasets:

thanhdath
/

mats-sql-bundle

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)	Selector pick	Notes
1-stage iter-2 uniform-temp	1524	64.96%	64.96%*	* selector currently leaks gold label — overstates
1-stage iter-2 mixed-temp (0.5/0.7/0.9/1.1)	1525	65.38%	65.38%*	best vanilla planner-only
3-stage iter-2 + 0.6B v_s/v_c + ORPO fixer iter-1	1525	65.05%	65.05%*	V+F neutral, 0 rescues
3-stage iter-2 + 0.5B v_s/v_c + replanner-fixer iter-2	1534 (in flight)	pending	pending	current eval, ETA 30 min

(*) The selector apply path had a gold-label leak (is_planner_correct used in prompt); patched in compute_bestofn_with_selector.py — re-run pending.

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)

Phase4 K=8 3-stage eval finishes (currently 89% done, ETA 30 min) → get pass@8 with replanner-fixer.
Apply patched (leak-free) selector to all K=8 JSONLs → get true selector accuracy (not oracle).
If pass@8 selector ≥67%: DONE. Write results table with per-agent sizes.
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.
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)