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wassname
f3f2c1250f
Streaming table (StepLogger) redesign per user review: - drop sprd/N/refr from the streaming view (constant / in argv / always '-') - short names: cos_pre->cin, cos_pre_s/t->cin_s/t, cos_post->cout, gradn->gn - 2 sig figs on loss; 1 on gn/lr - cin/cin_s/cin_t/cout/fired only on projecting arms (no vanilla cos_post_cf) - ADD per-mode cumulative student-hack columns hk_<rt|eq|xc|so|se|fm> on multi-mode (substrate) runs -> shows WHICH loophole classes are learnt - self-decoding legend() (only the columns this arm/mode-set shows) - end-dump auto-renders any (n,d) tuple as n/d; drops sprd/N too derisk_loopholes (#139): replace the 6 spoonfed exploit recipes with ONE generic elicit (the faithful hint already discloses the mechanism; the model must connect loophole+permission -> exploit = honest discoverability test) + an exit-interview '### Notes' section, surfaced in the log (too-vague/too-blatant hint signal). Rename ship->deploy (Gradient Routing): the route arm's quarantine-deleted eval is the DEPLOYED model; 'ablate' collided with the erase arm's gradient ablation. train.py columns + row dict + plot_dynamics + plot_route_evidence + results.py; dropped the dual-name back-compat reads. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
106 lines
4.5 KiB
Python
106 lines
4.5 KiB
Python
"""Single-run routing figure: training-time hack vs DEPLOYED-model hack.
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The routing story in one plot. During training the model keeps hacking (it runs
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with the quarantine knob ON, so the per-step hack_s curve climbs like vanilla).
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But the model we'd actually DEPLOY has the knob deleted -- its hack rate (the
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deploy-eval, measured every --eval-ablate-every steps) is what matters. If routing
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works, the deploy curve sits well BELOW the training curve at preserved solve.
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uv run python scripts/plot_route_evidence.py LOG.log --out out/route_evidence.png
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Reads the hack_deploy/solve_deploy columns (Gradient Routing deploy-eval).
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import tyro
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from projected_grpo.figs import link_latest
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def _frac(tok: str) -> float | None:
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if "/" in tok:
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a, b = tok.split("/")
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return int(a) / int(b) if int(b) else None
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try:
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v = float(tok)
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return None if v != v else v # NaN -> None
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except ValueError:
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return None
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def parse(log: Path):
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txt = log.read_text(errors="replace")
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hdr = next(l.split("| INFO |", 1)[1].split() for l in txt.splitlines()
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if "| INFO |" in l and "hack_s" in l and "refr" in l)
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idx = {n: i for i, n in enumerate(hdr)}
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i_step, i_train = idx["step"], idx["hack_s?"]
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i_solve = idx["gt_s↑"]
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i_hdep = idx["hack_deploy"]
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i_sdep = idx["solve_deploy"]
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steps, train_hack, solve_train = [], [], []
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deploy_step, deploy_hack, deploy_solve = [], [], []
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for l in txt.splitlines():
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if "| INFO |" not in l:
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continue
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r = l.split("| INFO |", 1)[1].split()
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if not r or not r[0].isdigit() or len(r) <= i_sdep:
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continue
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s = int(r[i_step])
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steps.append(s)
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train_hack.append(_frac(r[i_train]))
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solve_train.append(_frac(r[i_solve]))
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h = _frac(r[i_hdep])
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if h is not None: # deploy-eval only fires every N steps
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deploy_step.append(s); deploy_hack.append(h); deploy_solve.append(_frac(r[i_sdep]))
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return dict(steps=steps, train_hack=train_hack, solve_train=solve_train,
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deploy_step=deploy_step, deploy_hack=deploy_hack, deploy_solve=deploy_solve)
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def main(log: str, out: str = "out/figs/route_evidence.png") -> None:
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d = parse(Path(log))
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RED, GREY = "#b03a2e", "#9a8c7a" # hack=red (the story); solve=muted (context)
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fig, ax = plt.subplots(figsize=(7, 4))
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# Hack in red: training (knob on, solid) vs deployed (knob off, dashed+marker).
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# The vertical gap between the two reds at the last step IS the routing effect.
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ax.plot(d["steps"], d["train_hack"], color=RED, lw=2.2)
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ax.plot(d["deploy_step"], d["deploy_hack"], color=RED, lw=1.6, ls=(0, (4, 3)), marker="o", ms=4)
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ax.plot(d["deploy_step"], d["deploy_solve"], color=GREY, lw=1.4)
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# Direct labels at the right end (name + final value baked in) -> no legend,
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# no separate value annotations. One element does both jobs (eraser test).
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x_end = d["steps"][-1]
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def label(y, text, color):
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ax.annotate(text, (x_end, y), xytext=(8, 0), textcoords="offset points",
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va="center", color=color, fontsize=9)
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label(d["train_hack"][-1], f"hack, knob ON (training) {d['train_hack'][-1]:.0%}", RED)
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label(d["deploy_solve"][-1], f"solve, deployed {d['deploy_solve'][-1]:.0%}", GREY)
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label(d["deploy_hack"][-1], f"hack, knob OFF (deployed) {d['deploy_hack'][-1]:.0%}", RED)
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ax.set_ylim(-0.02, 1.0)
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ax.set_yticks([0, 0.5, 1.0]); ax.set_yticklabels(["0", ".5", "1"])
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ax.set_xticks([0, d["deploy_step"][-1] if d["deploy_step"] else x_end])
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ax.set_xlabel("GRPO step")
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ax.set_xlim(0, x_end * 1.5) # right margin for the direct labels
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for side in ("top", "right"):
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ax.spines[side].set_visible(False)
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ax.spines["left"].set_bounds(0, 1) # range-frame: axis spans the data
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ax.set_title("Routing parks the cheat in a deletable knob:\n"
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"the model hacks while training but the deployed model does not", fontsize=10.5)
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fig.tight_layout()
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Path(out).parent.mkdir(parents=True, exist_ok=True)
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fig.savefig(out, dpi=130)
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link = link_latest(Path(out))
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print(f"wrote {out} (docs/figs latest -> {link}) "
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f"(train_hack_final={d['train_hack'][-1]:.3f}, "
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f"deploy_hack_final={d['deploy_hack'][-1]:.3f}, deploy_solve_final={d['deploy_solve'][-1]:.3f})")
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if __name__ == "__main__":
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tyro.cli(main)
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