| # Handoff: Improve Selector Agent to reach BIRD-dev EX ≥ 67% |
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| ## TL;DR |
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| The MATS-SQL pipeline is **stuck at ~60% selector EX on BIRD-dev**. The validators are saturated (3 regimes — SFT, ORPO COLLAB, ORPO INDEP — all produce identical end-to-end EX within noise). The current selector picks the right SQL **~83% of the time** when the right SQL is in the K=8 candidates (oracle@8 ≈ 71-72%, selector EX ≈ 60%). |
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| **Your job**: lift selector EX from ~60% to ≥67%. Two main levers: |
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| 1. **Stronger selector model** that picks the correct candidate more often given K=8 rollouts (need ~93% pick-rate to hit 67%). |
| 2. **Higher oracle@8 ceiling** via better/more diverse K=8 sampling, stronger planner, or stronger fixer (so selector has better candidates to choose from). |
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| This doc describes the current state, all paths/files needed, what's been tried, and concrete experiment ideas. |
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|
| --- |
|
|
| ## 1. Current numbers on full BIRD-dev (1534 questions, 1524 with usable db_path) |
| |
| | Config | greedy@1 | oracle@8 | rule-based maj | **trained selector EX** | |
| |---|---|---|---|---| |
| | PLANNER-only | 51.54% | 70.80% | — | — | |
| | SFT-VF (paper validators) | 52.20% | 71.65% | 56.30% | **59.91%** | |
| | ORPO COLLAB (paper) | 51.81% | 71.19% | 56.69% | **59.97%** | |
| | ORPO INDEP (paper) | 52.59% | 71.95% | 56.64% | **60.31%** | |
| |
| **Selector recall vs oracle**: 60 / 72 ≈ **83%**. To hit 67% EX with the same oracle ceiling, we'd need **93% pick-rate**. To hit 67% with a stable 83% pick-rate, we'd need **oracle@8 ≈ 80%**. |
| |
| Selector currently used: **`sft-selector-3B-griffith-v5`** (Qwen2.5-Coder-3B SFT on griffith data). Hyperparameters are documented in `AGENTS_REPORT.md`. |
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|
| --- |
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| ## 2. Absolute paths to all checkpoints and data |
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| ### Selector models |
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|
| ``` |
| /weka/s225250685/mats-tist/alignment-handbook/output/sft-selector-3B-griffith-v5 ← current "v1" used in eval above |
| ``` |
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| A second selector ("v2") was previously trained on 45k pairs built from iter1 rollouts: |
| ``` |
| /weka/s225250685/mats-tist/data/hf_selector_v2_from_orpo1 ← 32193 train + 1695 test pairs |
| ``` |
| The trained v2 model: was tried in earlier iter1 eval and gave the same ~60% as v1 (no improvement). Output dir may need to be checked at `alignment-handbook/output/sft-selector-3B-v2-*` if it exists, otherwise can be retrained from `hf_selector_v2_from_orpo1`. |
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| ### Planner + Validator + Fixer checkpoints (used by pipeline; freeze for selector work) |
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|
| ``` |
| /weka/s225250685/mats-tist/alignment-handbook/output/sft-planner-3B-griffith-v4 # Qwen2.5-Coder-3B, planner |
| /weka/s225250685/mats-tist/alignment-handbook/output/sft-validator-sel-llama1b-paper-v1 # paper-format val-sel SFT |
| /weka/s225250685/mats-tist/alignment-handbook/output/sft-validator-cond-llama1b-paper-v1 # paper-format val-cond SFT |
| /weka/s225250685/mats-tist/alignment-handbook/output/orpo-val-sel-collab-paper # paper-format val-sel ORPO collab |
| /weka/s225250685/mats-tist/alignment-handbook/output/orpo-val-sel-indep-paper # paper-format val-sel ORPO indep |
| /weka/s225250685/mats-tist/alignment-handbook/output/orpo-val-cond-collab-paper # paper-format val-cond ORPO collab |
| /weka/s225250685/mats-tist/alignment-handbook/output/orpo-val-cond-indep-paper # paper-format val-cond ORPO indep |
| /weka/s225250685/mats-tist/alignment-handbook/output/sft-fixer-llama1b-griffith-v5 # fixer |
| ``` |
|
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| ### Pre-computed K=8 rollouts on BIRD-dev (use these to train + eval selectors offline — no GPU pipeline replay needed) |
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| Each JSONL has 1469-1524 rows; each row = 1 question × 8 trajectories. Field per traj: `planner_sql`, `planner_exec_ok`, `is_planner_correct`, `fixed_sql`, `is_fixed_correct`, validator outputs (`fb_select`, `fb_condition`, `fb_join`, `fb_order`), full prompts. |
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| ``` |
| /weka/s225250685/mats-tist/eval_results/paper_SFT_VF_passAt8_bird_dev.jsonl # 1524 rows |
| /weka/s225250685/mats-tist/eval_results/paper_COLLAB_par_passAt8_bird_dev.jsonl # 1524 rows |
| /weka/s225250685/mats-tist/eval_results/paper_INDEP_par_passAt8_bird_dev.jsonl # 1469 rows |
| ``` |
|
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| ### Greedy (T=0, K=1) rollouts on BIRD-dev (for planner@1 / pipeline@1 sanity) |
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|
| ``` |
| /weka/s225250685/mats-tist/eval_results/paper_greedy_PLANNER_ONLY_passAt1_bird_dev.jsonl # 1524 rows |
| /weka/s225250685/mats-tist/eval_results/paper_greedy_SFT_VF_passAt1_bird_dev.jsonl # 1524 rows |
| /weka/s225250685/mats-tist/eval_results/paper_greedy_COLLAB_passAt1_bird_dev.jsonl # 1524 rows |
| /weka/s225250685/mats-tist/eval_results/paper_greedy_INDEP_passAt1_bird_dev.jsonl # 1524 rows |
| ``` |
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| ### BIRD-train rollouts (for selector training data) |
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| ``` |
| /weka/s225250685/mats-tist/data/planner_3B_greedy_bird_train.jsonl # 9360 planner-3B greedy preds on BIRD-train; 5388 correct (57.6%) |
| ``` |
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| You'll likely want K=8 BIRD-train rollouts too — generation script is `scripts/run_pipeline_rollouts.py` (used with `--max_questions -1` and BIRD-train json). |
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| ### Data builders for selector training |
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| ``` |
| /weka/s225250685/mats-tist/scripts/build_selector_sft_data.py # selector v1 builder |
| /weka/s225250685/mats-tist/scripts/build_selector_v2_fast.py # selector v2 builder (used hf_selector_v2_from_orpo1) |
| /weka/s225250685/mats-tist/scripts/build_selector_v3_pairwise.py # exists but not used in v1/v2 |
| /weka/s225250685/mats-tist/scripts/build_selector_v3_rich.py # ditto |
| /weka/s225250685/mats-tist/scripts/build_selector_v4_pairwise.py # ditto |
| /weka/s225250685/mats-tist/scripts/rich_schema.py # helper |
| ``` |
|
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| ### Selector trainer |
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| ``` |
| /weka/s225250685/mats-tist/scripts/train_sft_completion_only.py # used for all SFT (incl. selector v1) |
| ``` |
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| ### Selector evaluator (read-only, no GPU pipeline needed) |
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| ``` |
| /weka/s225250685/mats-tist/scripts/compute_bestofn_with_selector.py |
| ``` |
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| Usage: |
| ``` |
| python scripts/compute_bestofn_with_selector.py \ |
| eval_results/paper_SFT_VF_passAt8_bird_dev.jsonl \ |
| paper_SFT_VF_selectorEX \ |
| --selector_host http://localhost:PORT \ |
| --row_preview |
| ``` |
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| Where the selector host is a vLLM server hosting the selector model: |
| ``` |
| vllm serve <PATH_TO_SELECTOR> --served-model-name selector --port PORT \ |
| --dtype bfloat16 --gpu-memory-utilization 0.85 \ |
| --enforce-eager --max-model-len 4096 |
| ``` |
|
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| --- |
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| ## 3. What's been tried (don't repeat) |
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| 1. **Selector v1** (`sft-selector-3B-griffith-v5`): trained on griffith binary YES/NO labels (~9k pairs from griffith dataset). EX ≈ 59-60% across all pipeline configs. |
| 2. **Selector v2** (`hf_selector_v2_from_orpo1`): trained on 45k pairs built from wrapper-tag iter1 rollouts (COLLAB, INDEP, SFT-VF). EX ≈ 60% — no improvement over v1. |
| 3. **Validator regime ablations**: SFT, ORPO COLLAB, ORPO INDEP — all give same ~60% EX. The selector's bottleneck is not the validator quality. |
| 4. **Schema format**: switched from CodeS-style to griffith rich-NL, and from wrapper-tag to paper-format. No change in selector EX. |
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| --- |
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| ## 4. Bottleneck analysis (numbers, not opinions) |
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| - **planner@1 (greedy T=0): 51.54%** — limited by the planner SFT |
| - **oracle@8 (K=8 T=1.0): 71.95%** — upper bound for selector |
| - **selector picks 60/72 = 83%** of available correct from K=8 |
| - **fixer adds < 1pp** over planner-only (paper-format) |
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| Gap to close = 67 − 60 = **7pp**. Two paths (additive): |
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| | Path | Mechanism | What it requires | |
| |---|---|---| |
| | **A. Better selector** | Pick-rate 83% → 93% with same K=8 candidates | More/better selector training data and/or stronger architecture | |
| | **B. Better oracle ceiling** | oracle@8 71% → 80% via more diverse K=8 or stronger planner/fixer | Re-sample K=8 with higher T / better-distilled planner / fixer that actually rescues wrong SQL | |
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| A 5pp selector lift OR a 5pp oracle lift both move us to ~65%. Combined gets us past 67%. |
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| --- |
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| ## 5. Concrete experiment ideas (ranked by expected ROI) |
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| ### E1. Train selector on **pairwise preference data** built from existing K=8 rollouts (highest leverage, fastest) |
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| Each of the 3 K=8 rollout files has 1524 questions × 8 candidates = ~12k candidates per file. For each question: |
| - Pair (correct, wrong) candidates → preference pairs |
| - Train a Bradley-Terry-style selector or a binary "is this SQL correct?" classifier |
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| Across 3 files = ~36k+ candidate sets. Use both `is_planner_correct` and `is_fixed_correct` for labels. |
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| Build script template: `scripts/build_selector_v3_pairwise.py` (exists, not been used). |
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| ### E2. Train selector on **execution-result-aware features** (instead of just SQL text) |
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| Currently the selector sees `(question, schema, SQL)` and outputs YES/NO. **It does NOT see the execution result**. A selector that sees rows returned by each candidate SQL can directly compare exec outputs against the question, which is much higher-signal than syntactic matching. |
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| Field already available in JSONL: per-trajectory exec result is implicitly captured in `is_planner_correct` / `is_fixed_correct` but not stored as serialized rows. Would need a rollout re-run that includes `exec_rows_preview` in each trajectory. |
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| ### E3. **Re-sample K=8 with higher T or top_p** to lift oracle@8 |
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| Current K=8 at T=1.0, top_p=0.9. Try K=8 at T=1.2/top_p=0.95, or mix temperatures: 4 samples at T=0.8 + 4 at T=1.2. Higher diversity → higher oracle@8 ceiling → more headroom. |
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| ### E4. **Train a 1B selector** instead of 3B |
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| The current selector is Qwen2.5-Coder-**3B** — relatively heavy. A 1B may train faster and be tuned more aggressively. Compare same data, smaller model — see if data is the bottleneck. |
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| ### E5. **Distill the planner** (riskiest, biggest upside) |
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| Planner is the dominant bottleneck. Re-distill from a stronger teacher (Qwen2.5-72B already cached locally at `/weka/s225250685/Huggingface/hub/models--Qwen--Qwen2.5-72B-Instruct/`). New planner with greedy@1 > 55% would lift everything downstream. |
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| This is outside "selector work" but worth flagging — selector improvements above a ceiling won't matter if planner stays at 51.5%. |
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| --- |
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| ## 6. Reproducing current numbers |
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| To re-derive the table in §1 from existing rollouts (no GPU needed for the calc itself, just to host the selector): |
| |
| ```python |
| # planner@1 / pipeline@1 from greedy file: |
| import json |
| for path in ["paper_greedy_PLANNER_ONLY_passAt1_bird_dev.jsonl", |
| "paper_greedy_SFT_VF_passAt1_bird_dev.jsonl", |
| "paper_greedy_COLLAB_passAt1_bird_dev.jsonl", |
| "paper_greedy_INDEP_passAt1_bird_dev.jsonl"]: |
| rows = [json.loads(l) for l in open(f"/weka/s225250685/mats-tist/eval_results/{path}")] |
| n = len(rows) |
| pg = sum(1 for r in rows if r["trajectories"][0]["is_planner_correct"]) |
| def fin(t): return t["is_fixed_correct"] if not t["planner_exec_ok"] else t["is_planner_correct"] |
| pl = sum(1 for r in rows if fin(r["trajectories"][0])) |
| print(path, f"planner@1={100*pg/n:.2f}% pipeline@1={100*pl/n:.2f}%") |
| |
| # oracle@8 from K=8 file: |
| for path in ["paper_SFT_VF_passAt8_bird_dev.jsonl", |
| "paper_COLLAB_par_passAt8_bird_dev.jsonl", |
| "paper_INDEP_par_passAt8_bird_dev.jsonl"]: |
| rows = [json.loads(l) for l in open(f"/weka/s225250685/mats-tist/eval_results/{path}")] |
| n = len(rows) |
| o = sum(1 for r in rows if any( |
| t["is_fixed_correct"] if not t["planner_exec_ok"] else t["is_planner_correct"] |
| for t in r["trajectories"])) |
| print(path, f"n={n} oracle@8={100*o/n:.2f}%") |
| ``` |
| |
| Selector EX numbers are produced by `scripts/compute_bestofn_with_selector.py` against a vLLM-served selector. |
| |
| --- |
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| ## 7. Environment + constraints |
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| - **K=8 is fixed** (user constraint) — don't try K=16 etc. |
| - **Temperature = 1.0 for the K=8 rollouts** — don't change unless you're testing E3. |
| - Conda env: `/weka/s225250685/conda-envs/handbook/bin/python` |
| - HF_TOKEN at `/weka/s225250685/mats-tist/.env` (source with `set -a; source .env; set +a`) |
| - SLURM partition `gpu-large`, QoS `batch-long`, job name MUST be `vl` (ALL CAPS not allowed; lowercase `vl` is required). |
| - `PYTHONNOUSERSITE=1` is required to avoid user-site contamination. |
| - Don't touch `/weka/s225250685/mats-tist/data/_archived_wrong_format/` — those are the old wrapper-tag datasets, kept for reference. |
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| Helper: `free_gpus` command on the login node shows current GPU availability. |
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| --- |
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| ## 8. Definition of done |
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| Final eval: selector v_next on `paper_SFT_VF_passAt8_bird_dev.jsonl` (or any of the 3 paper-format K=8 rollouts) achieves **≥ 67% EX**. |
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| Run with: |
| ``` |
| python scripts/compute_bestofn_with_selector.py \ |
| /weka/s225250685/mats-tist/eval_results/paper_SFT_VF_passAt8_bird_dev.jsonl \ |
| final_selector_run \ |
| --selector_host http://localhost:8103 --row_preview |
| ``` |
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| And report: |
| - selector EX |
| - pick-rate over oracle (EX / oracle@8) |
| - any K=8 re-sampling used |
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