wassname/evil_MoE
1
0
Fork 0
mirror of https://github.com/wassname/evil_MoE.git synced 2026-08-11 11:18:24 +08:00
Code Issues Packages Projects Releases Wiki Activity

log: rename route ablated-eval -> SHIP-eval, self-evident with SHOULD

Browse Source 
User couldn't parse 'route ablated-eval (delta_S_hack=0)'. Common terms now:
- per-step line: 'SHIP-eval (quarantine knob OFF = deployed model)' + inline
  SHOULD (ship hack < training hack_s if the knob holds the cheat).
- columns hack_abl/solve_abl -> hack_ship/solve_ship.
- final BLUF: 'train/knob-on' vs 'ship/knob-off' + SHOULD (quarantine absorbed
  the cheat). plot_dynamics accepts both old+new names. smoke-route green.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
wassname
2026-05-30 03:01:04 +00:00
co-authored by Claude Opus 4.8
parent 969c724d9d
commit 4f9651b1f3
2 changed files with 43 additions and 34 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scripts/plot_dynamics.py
+16 -12
View File
@@ -20,9 +20,9 @@ Arm classification (from the preset line `arm=`, covering old --arm and new
online erasure arm=projected, --vhack-refresh-every=N>0 (re-extracted)
routing arm=routing (intervention=route)
For routing we plot the ABLATED-eval hack/solve (hack_abl/solve_abl, measured
with delta_S_hack zeroed every --eval-ablate-every steps), NOT the training-time
hack_s: the routed forward still hacks during training, so the training curve
For routing we plot the SHIP-eval hack/solve (hack_ship/solve_ship, the deployed
model = quarantine knob deleted, measured every --eval-ablate-every steps), NOT
the training-time hack_s: the routed forward still hacks during training, so the training curve
would falsely read "route doesn't work". The ablated curve is the deployment
model. (none/erase plot training-time hack_s; their intervention acts at train
time.)
@@ -91,10 +91,12 @@ def parse_log(path: Path) -> dict | None:
series: dict[str, list[float]] = defaultdict(list)
steps: list[int] = []
# Also parse the route ablated-eval columns when present (older logs lack
# them -> skip). For routing we plot THESE, not the training-time hack_s.
abl = {"hack_abl", "solve_abl"} & set(idx)
wanted = {**RATE_COLS, **COS_COLS, **{c: c for c in abl}}
# Also parse the route SHIP-eval columns when present (older logs lack them
# -> skip). For routing we plot THESE (deployed model), not training-time
# hack_s. Renamed hack_abl/solve_abl -> hack_ship/solve_ship 2026-05-30;
# accept both so old evidence logs still parse.
ship = {"hack_abl", "solve_abl", "hack_ship", "solve_ship"} & set(idx)
wanted = {**RATE_COLS, **COS_COLS, **{c: c for c in ship}}
for line in txt.splitlines():
if "| INFO |" not in line:
continue
@@ -109,12 +111,14 @@ def parse_log(path: Path) -> dict | None:
run = dict(arm=arm, refr=refr, seed=seed, vhack=vhack,
steps=np.array(steps), **{k: np.array(v, dtype=float) for k, v in series.items()})
# COHERENCE-GAP FIX: route's training-time hack_s looks vanilla (the routed
# forward still hacks); routing's benefit only shows once delta_S_hack is
# ablated at eval. So for routing, plot the ablated series under the same
# forward still hacks); routing's benefit only shows on the DEPLOYED model
# (quarantine knob deleted). So for routing, plot the ship series under the
# hack_s/gt_s keys -> all downstream (panels, onset, overlay) reads it.
if arm == "routing" and "hack_abl" in run:
run["hack_s"] = run["hack_abl"]
run["gt_s"] = run["solve_abl"]
if arm == "routing":
hk = "hack_ship" if "hack_ship" in run else "hack_abl" if "hack_abl" in run else None
if hk:
run["hack_s"] = run["hack_ship" if "hack_ship" in run else "hack_abl"]
run["gt_s"] = run["solve_ship" if "solve_ship" in run else "solve_abl"]
return run
src/projected_grpo/train.py
+27 -22
View File
@@ -583,8 +583,8 @@ class StepLogger:
# refresh fired (frozen-V and vanilla runs are all "-").
_Col("refr", 9, "refr", None),
# Route-only ablated-eval hack/solve (delta_S_hack=0); nan elsewhere.
_Col("hack_abl", 8, "hack_abl", "+.3f"),
_Col("solve_abl", 9, "solve_abl", "+.3f"),
_Col("hack_ship", 9, "hack_ship", "+.3f"), # deployed model (quarantine off)
_Col("solve_ship", 10, "solve_ship", "+.3f"),
]
def header(self) -> str:
@@ -1307,11 +1307,13 @@ table columns:
model.train()
refr = f"{len(v_hack)}/{sum(V.shape[0] for V in v_hack.values())}" # mod/axes -> per-step row
# Periodic route ablated-eval: zero the quarantine, eval hack/solve on the
# fixed subset. This is the curve the plot uses for route (training-time
# hack_s looks vanilla; the routed forward still hacks). NaN on non-eval
# steps and for non-route arms (plot's EMA holds NaN).
hack_abl = solve_abl = float("nan")
# Periodic SHIP-eval (routing): delete the quarantine knob and eval the
# DEPLOYED model on a fixed subset. Routing's claim is that the cheating
# capability lands in the quarantine, so deleting it (= what we ship)
# should hack much less than the training-time model (the per-step hack_s
# row, which still hacks because training keeps the knob on). This is the
# curve the plot uses for route. NaN on non-eval steps / non-route arms.
hack_ship = solve_ship = float("nan")
if (cfg.intervention == "route" and cfg.eval_ablate_every > 0
and (step % cfg.eval_ablate_every == 0 or step == steps - 1)):
_was_training = model.training
@@ -1320,9 +1322,12 @@ table columns:
ev = eval_hack_solve(model, tok, problems, eval_idxs, gen_cfg_eval, device, max_new)
if _was_training:
model.train()
hack_abl, solve_abl = ev["hack"], ev["solve"]
logger.info(f"step {step} route ablated-eval (delta_S_hack=0): "
f"hack={hack_abl:.3f} solve={solve_abl:.3f} (n={ev['n']})")
hack_ship, solve_ship = ev["hack"], ev["solve"]
logger.info(
f"step {step} SHIP-eval (quarantine knob OFF = deployed model): "
f"hack={hack_ship:.3f} solve={solve_ship:.3f} n={ev['n']}. "
f"SHOULD: ship hack < this step's training hack_s (knob is holding "
f"the cheat); ELSE routing isn't capturing it")
rewards_t = torch.tensor(agg_rew, dtype=torch.float32) if agg_rew else torch.zeros(1)
rew_mean = rewards_t.mean().item()
@@ -1434,8 +1439,8 @@ table columns:
# Route ablated-eval (delta_S_hack=0); NaN except on route eval steps.
# Appended AFTER refr so results.py's positional GT_S/HACK_S indices
# are unaffected. plot_dynamics reads it by name.
"hack_abl": hack_abl,
"solve_abl": solve_abl,
"hack_ship": hack_ship,
"solve_ship": solve_ship,
"gen": t_gen,
"fb": t_fb,
"t_rew": t_rew,
@@ -1514,20 +1519,20 @@ table columns:
if cfg.intervention == "route":
assert dsh_norm > 0.0, "route: delta_S_hack never moved -> degenerated to erasure"
# Route: final kept-vs-ablated eval -- the absorption test. KEPT keeps the
# quarantine (training-time model, still hacks); ABLATED zeroes it (the
# deployment model). SHOULD: ablated hack < kept hack at preserved solve
# => the quarantine absorbed the hack. ELSE routing didn't localize it.
# Route: final training-vs-shipped eval -- the absorption test. TRAIN keeps
# the quarantine knob on (training-time model, still hacks); SHIP deletes it
# (the deployed model). SHOULD: ship hack < train hack at preserved solve
# => the quarantine knob absorbed the cheat. ELSE routing didn't localize it.
if cfg.intervention == "route":
model.eval()
ev_kept = eval_hack_solve(model, tok, problems, eval_idxs, gen_cfg_eval, device, max_new)
ev_train = eval_hack_solve(model, tok, problems, eval_idxs, gen_cfg_eval, device, max_new)
with ablate_quarantine(wrappers):
ev_abl = eval_hack_solve(model, tok, problems, eval_idxs, gen_cfg_eval, device, max_new)
ev_ship = eval_hack_solve(model, tok, problems, eval_idxs, gen_cfg_eval, device, max_new)
logger.info(
f"ROUTE EVAL (n={ev_kept['n']}): "
f"kept hack={ev_kept['hack']:.3f} solve={ev_kept['solve']:.3f} | "
f"ablated hack={ev_abl['hack']:.3f} solve={ev_abl['solve']:.3f} "
f"(SHOULD: ablated hack < kept hack at ~matched solve)")
f"ROUTE EVAL (n={ev_train['n']}): "
f"train/knob-on hack={ev_train['hack']:.3f} solve={ev_train['solve']:.3f} | "
f"ship/knob-off hack={ev_ship['hack']:.3f} solve={ev_ship['solve']:.3f} "
f"(SHOULD: ship hack < train hack at ~matched solve => quarantine absorbed the cheat)")
# Final tail: cue emoji + main metric BLUF, then per-step tsv table.
# Vanilla arm: 🟢 if hacking emerged. Projected arm: 🟢 if HACK_RATE dropped