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chore: memory updates, diag_pairs_compare script
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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wassname
@@ -9,3 +9,4 @@
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- [pueue negative-priority gotcha](pueue-negative-priority-gotcha.md) — `pueue add` negative prio needs `-o=-N` attached; `-o -N` silently fails the add.
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- [Rename on logic change](feedback_rename_on_logic_change.md) — when an arm's logic changes (binary->banded gate), give it a new id (routeV/route3), not just a tag suffix; else old/new runs are uncomparable.
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- [Check paper before diagnosing](feedback_check_paper_before_diagnosing.md) — re-read source for expected number/horizon before "experiment is broken"; paper: hack emerges on-policy at step 80-100, base solves ~12-20% not 94%.
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- [Eval must be recency-clean](project_eval_must_be_recency_clean.md) — eval on paper test set (ids>=3243, base ~0.1), NOT the holdout/first-N (memorized->0.94); train seeded-shuffle not first-N. Run set: `just queue-dir6`/`queue-broad`.
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---
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name: project_eval_must_be_recency_clean
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description: "eval AND train must use the paper's recency-held-out test set / seeded-shuffle -- NOT first-N-by-id or the holdout file (pretraining-memorized -> base solve 0.94 instead of ~0.12)"
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metadata:
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node_type: memory
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type: project
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originSessionId: c06c2b0e-44ee-45a1-b213-4f77c006109c
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---
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The base-solve=0.94 anomaly (2026-06-07) was an eval-set bug, fixed in commits
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3fad736 + da8b846. Durable rules for this repo:
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- **Eval on the paper's own `leetcode_test_medhard.jsonl`** (119, ids >= 3243).
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It is the only recency-held-out split; base Qwen3-4B solves ~9-12% there
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(matches paper fn9; job 176 measured test=0.094, train_filtered=0.203). The
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periodic curve is a 32-sample of it, the final number is the full 119.
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- **NEVER eval on `leetcode_train_medhard_holdout.jsonl`** (353, our artifact).
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Disjoint from train by id but in the train id/recency range (ids 3-3205, 88%
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medium) = classic problems Qwen3-4B memorized in pretraining -> base solve
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0.94, which saturates solve and kills the hack metric's gt-fail headroom.
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"Disjoint by id" controls for TRAIN leakage, NOT pretraining MEMORIZATION.
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- **Train pool: seeded-shuffle, not first-N-by-id.** Lowest ids are the oldest,
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most-memorized problems; first-200-by-id = the easiest 200 -> weak hack
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incentive. Sample seeded-random and PIN the teacher-seed ids (else seeding
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no-ops). train.py ~682.
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- Hint is the paper's `simple_overwrite_tests` (data.py:37), NOT the easier
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`_detailed`/`_aware` variants. Model = Qwen/Qwen3-4B (= ref qwen/Qwen3-4B).
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- max_new=512 (fast) mildly undershoots solve vs paper's 1536; 0.094 vs 0.12 is
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the truncation tax, acceptable.
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Run set lives in justfile: `just queue-dir6 SEED` (8-arm single-seed
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directionality set) and `just queue-broad` (3-seed headline + ablations). Full
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us-vs-reference table: docs/spec/20260607_eval_contamination_fix.md. See
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[[feedback_check_paper_before_diagnosing]], [[project_paper_comparability_verdict]].
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"""Pairs comparison: which PAIR-SET gives the best high-precision hack direction?
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Companion to diag_cosine_dist.py. That script sweeps SCORE x SPACE x FILTER for ONE
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pair-set; this one fixes the winning config (grad cosine, the p@10=0.70 corner) and
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sweeps the PAIR-SET axis -- authored vs pool-derived (prog_wide family) vs intent.
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This is the table I should have built before committing a GPU run to a pairs guess.
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The expensive part (140 live-rollout backward passes) is independent of the pairs, so
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we cache the per-module live grads ONCE and loop the cheap v_grad build + scoring over
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pair-sets. No oracle labels touch training; `exploited` is an offline plot-only label.
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uv run python -m scripts.diag_pairs_compare # 4B, free GPU, ~20 min
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outputs: out/diag/pairs_compare.csv (pairset x AUROC/p@10/p@20 at grad cosine).
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"""
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from __future__ import annotations
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import json
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import struct
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from dataclasses import dataclass
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from pathlib import Path
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import torch
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import tyro
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import polars as pl
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from loguru import logger
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from tabulate import tabulate
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from safetensors.torch import load_file
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from vgrout.antipasto import wrap_model_with_antipasto
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from vgrout.extract_vhack_grad import extract_v_hack, completion_nll
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from vgrout.pairs import PAIRS
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from vgrout.pairs_v2 import PAIRS_V2
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from vgrout.pairs_intent import PAIRS_FUNCNAME
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from vgrout.pairs_from_pool import load_pairs_json
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from vgrout.train import CACHE_ROOT
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from scripts.diag_cosine_dist import _auroc, _prec_at_k
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_PS = Path("out/pairsets")
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# every label here lives on hand-authored pairs OR pool demos we wrote -- no live labels.
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PAIRSETS = {
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"authored_all": lambda: list(PAIRS), # 18 pairs / 6 axes
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"authored_runtests": lambda: list(PAIRS)[:8], # axis-1 only (the live mechanism)
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"authored_v2": lambda: list(PAIRS_V2), # 6 harder/verbose
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"authored_allv2": lambda: list(PAIRS) + list(PAIRS_V2),
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"funcname": lambda: list(PAIRS_FUNCNAME), # best intent design
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"prog_wide": lambda: load_pairs_json(_PS / "prog_wide.json"), # 30, training default
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"prog_wider": lambda: load_pairs_json(_PS / "prog_wider.json"),
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"prog_widest": lambda: load_pairs_json(_PS / "prog_widest.json"),
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"heldout_known_rt": lambda: load_pairs_json(_PS / "heldout_known_runtests.json"),
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}
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@dataclass
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class Cfg:
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run_dir: Path = Path("out/runs/20260607T134234_fast_routingV_seed43_dir6_routeV_pertoken_s43")
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ckpt: str = "first_hack"
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step_lo: int = 5
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step_hi: int = 9
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max_rollouts: int = 140
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out_dir: Path = Path("out/diag")
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def main(cfg: Cfg) -> int:
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device = torch.device("cuda")
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kept_path = cfg.run_dir / f"{cfg.ckpt}.safetensors"
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hack_path = cfg.run_dir / f"{cfg.ckpt}_hack.safetensors"
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with open(kept_path, "rb") as f:
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meta = json.loads(f.read(struct.unpack("<Q", f.read(8))[0])).get("__metadata__", {})
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model_name = meta.get("model", "Qwen/Qwen3-4B")
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logger.info(f"ckpt {kept_path.name} step={meta.get('step')} model={model_name}")
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tok = AutoTokenizer.from_pretrained(model_name)
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if tok.pad_token_id is None:
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tok.pad_token = tok.eos_token
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model = AutoModelForCausalLM.from_pretrained(
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model_name, dtype=torch.bfloat16, attn_implementation="flash_attention_2").to(device)
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model.config.use_cache = False
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wrappers = wrap_model_with_antipasto(model, model_name, CACHE_ROOT, device, grad_probe=False)
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names = sorted(wrappers)
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kept, hack = load_file(str(kept_path)), load_file(str(hack_path))
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for nm in names:
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wrappers[nm]["delta_S"].data.copy_(kept[nm].to(device))
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wrappers[nm]["delta_S_hack"].data.copy_(hack[nm].to(device))
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model.eval()
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# ── cache live-rollout per-module grads ONCE (independent of pair-set) ──
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recs = [json.loads(l) for l in (cfg.run_dir / "rollouts.jsonl").read_text().splitlines()]
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batch = [r for r in recs if cfg.step_lo <= r["step"] <= cfg.step_hi and r["text"].strip()][:cfg.max_rollouts]
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gb_cache, gn_cache, labels = [], [], []
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for i, r in enumerate(batch):
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model.zero_grad(set_to_none=True)
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loss = completion_nll(model, tok, r["prompt"], r["text"], device)
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if not torch.isfinite(loss):
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continue
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loss.backward()
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gb = {nm: wrappers[nm]["delta_S"].grad.flatten().float().cpu() for nm in names}
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gb_cache.append(gb)
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gn_cache.append(torch.tensor([gb[nm].norm().item() for nm in names])) # |g_m|, pairs-independent
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labels.append(bool(r["exploited"]))
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if (i + 1) % 40 == 0:
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logger.info(f" cached {i+1}/{len(batch)} live grads")
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model.zero_grad(set_to_none=True)
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gn_stack = torch.stack(gn_cache) # [n_roll, n_mod]
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n_pos = sum(labels)
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logger.info(f"live: {len(labels)} rollouts, {n_pos} exploited ({n_pos/len(labels):.2f} base rate)")
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# ── score one pair-set at grad cosine (all + keep75 noise filter) ──
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def score_pairset(pairs):
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_, v_sv, raw_grads, _ = extract_v_hack(
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model, tok, wrappers, pairs, top_k=1, tau_axis=0.0, n_heldout=2, device=device)
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v_grad = {nm: (lambda d: d / d.norm().clamp_min(1e-12))(
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(raw_grads[f"hack/{nm}"] - raw_grads[f"clean/{nm}"]).mean(0).flatten().float().cpu())
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for nm in names}
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sv0 = torch.tensor([v_sv[nm][0].item() for nm in names])
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keep75 = sv0 >= sv0.quantile(0.25)
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cos_all, cos_k75 = [], []
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for gb, gn in zip(gb_cache, gn_stack):
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dot = torch.tensor([(gb[nm] @ v_grad[nm]).item() for nm in names])
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for mask, out in [(torch.ones(len(names), dtype=bool), cos_all), (keep75, cos_k75)]:
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d = dot[mask].sum()
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gmag = gn[mask].pow(2).sum().sqrt()
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nmod = int(mask.sum())
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out.append((d / (gmag.clamp_min(1e-12) * nmod ** 0.5)).item())
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return cos_all, cos_k75
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rows = []
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for name, fn in PAIRSETS.items():
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try:
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pairs = fn()
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except FileNotFoundError:
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logger.warning(f"{name}: pairset file missing, skipping")
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continue
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model.eval()
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ca, ck = score_pairset(pairs)
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row = dict(pairset=name, n=len(pairs),
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AUROC=round(_auroc(ca, labels), 3),
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p10=round(_prec_at_k(ca, labels, 10), 3),
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p20=round(_prec_at_k(ca, labels, 20), 3),
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p10_keep75=round(_prec_at_k(ck, labels, 10), 3))
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rows.append(row)
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logger.info(f"{name} (n={len(pairs)}): grad-cosine p10={row['p10']} AUROC={row['AUROC']}")
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df = pl.DataFrame(rows).sort(["p10", "AUROC"], descending=True)
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cfg.out_dir.mkdir(parents=True, exist_ok=True)
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df.write_csv(cfg.out_dir / "pairs_compare.csv")
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print("\n=== pair-sets ranked by grad-cosine precision@10 (base rate "
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f"{n_pos/len(labels):.2f}, {len(labels)} live rollouts) ===")
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print(tabulate(df.to_pandas(), headers="keys", tablefmt="pipe", showindex=False))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main(tyro.cli(Cfg)))
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