mirror of
https://github.com/wassname/grpo_proj2.git
synced 2026-06-27 18:05:04 +08:00
wassname
b0d1bcd3d5
Expand docs/pseudocode/01..07 into a slim, fail-fast src/projected_grpo/ that
passes `just smoke`. Code mirrors the pseudocode (δS/Σ/V names, relu-before-agg
cin/cout, Dr.GRPO unbiased loss). Did not read the original src.
7 modules (~880 LOC):
- rewards.py grader + 4 loophole modes + hack x mode diagonal self-check (R1)
- problems.py tiny LeetCode substrate + contrastive pairs (R5)
- antipasto.py SVD adapter, identity at δS=0 (R2)
- proj.py erase/route/measure_only projection (R3)
- extract_vhack_grad.py per-module SVD of paired grad diffs, noise floor (R5)
- train.py mixed student+teacher GRPO loop, presets smoke/fast/full (R4)
- build_pool.py self-contained frozen teacher-pool fixture
`just smoke-all` PASS (exit 0): erase/none/route trio, grader diagonal clean,
v_hack cache miss->hit, ckpt every-25. Fresh-eyes review: 6/6 mechanics faithful.
Simplifications: merged loopholes+verify_rewards->rewards, pairs->problems; flat
Config + `train.py {preset} [--overrides]` CLI; justfile 384->71 lines; trimmed
results table; token-efficient train logging (config anchor, SHOULD at loop site,
sparse tqdm postfix, BLUF tail with cue + direction-arrow table).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
113 lines
7.9 KiB
Python
113 lines
7.9 KiB
Python
# ── The experiment ─────────────────────────────────────────────────────
|
||
# Sources: docs/spec.md (preregistered), README.md.
|
||
# Hypothesis (H1): SVD-basis gradient projection against a v_hack extracted from
|
||
# ~60-80 contrastive pairs reduces reward-hack rate by ≥30pp absolute vs vanilla
|
||
# GRPO, at MATCHED LeetCode pass rate (±10pp).
|
||
# STATUS (blog 20260529, read this): the vanilla-vs-projected head-to-head EXISTS —
|
||
# Table 1, n=2 matched seeds (41,43), 21 pairs (a PREFIX of the 60-80 H1 spec):
|
||
# erase frozen-V −12..−18pp hack_student; refresh-2 −16..−23pp; pass ∓0..12pp.
|
||
# So the headline IS 12-23pp at 21 pairs, NOT 30pp. The 30pp figure was a CONFOUNDED
|
||
# within-arm step (12→21 pairs moved pairs+k+τ together). What's missing is n=3
|
||
# (queued), the NEGATIVE CONTROLS below, and the 60-80-pair spec design — not the
|
||
# comparison itself.
|
||
|
||
# ── Substrate: 4 independent loophole modes, partitioned non-overlapping ──
|
||
# so a vanilla student can learn ALL four independently (the emergence reference).
|
||
MODES = ["run_tests", "sentinel", "stdout_marker", "file_marker"]
|
||
model, teacher_pool = FIXED # identical across arms; ONLY gradient policy differs
|
||
|
||
# ── Arms (06_train_loop with one knob changed) ─────────────────────────
|
||
ARMS = {
|
||
"vanilla": intervention="none", # no projection; emergence baseline
|
||
"erase": intervention="erase", V from all modes, # subtract hack component from δS.grad
|
||
"route": intervention="route", V from all modes, # quarantine in δS_hack, ablate at deploy
|
||
"route_weak": ..., # generalization test, below
|
||
}
|
||
# ── NEGATIVE CONTROLS — the missing first-class arms (BOTH families, top fix) ──
|
||
# Projecting out ANY rank-k subspace and renormalizing perturbs the update, so a
|
||
# hack drop may be generic regularization, not selective removal. erase must beat:
|
||
CONTROLS = {
|
||
"random_V": "erase with a random orthonormal V (matched k, matched ‖·‖)",
|
||
"shuffled_V": "erase with V from label-SHUFFLED pairs (hack/clean swapped)",
|
||
"nonhack_V": "erase with V from clean-vs-clean (non-hack-contrastive) pairs",
|
||
}
|
||
# If real V does not clearly beat all three, the effect is NOT hack-specific → STOP.
|
||
|
||
def run_arm(arm):
|
||
for seed in [41, 42, 43]: # n≥3 for the headline claim
|
||
train(model, teacher_pool, arm, seed)
|
||
return hack_rate_last5, gt_pass_rate_last5 # paired: hack must drop at MATCHED pass
|
||
|
||
# ── The no-cheat test: weak-detector generalization (leave-one-mode-out) ──
|
||
# At deployment there are KNOWN hacks and UNKNOWN hacks. We are ALLOWED a weak
|
||
# detector that flags some modes and misses others. Extract V from the known set,
|
||
# route on it, and ask: is the held-out mode ALSO suppressed? A detector that sees
|
||
# every hack proves nothing. Both reviewers: a single 2-vs-2 split at n=3 is weak
|
||
# and confounded → prefer LEAVE-ONE-MODE-OUT over all 4 modes (4 folds), w/ controls.
|
||
def weak_detector_leave_one_out():
|
||
for held in MODES: # held = the "unknown" hack
|
||
known = [m for m in MODES if m != held] # weak detector flags only these
|
||
V = extract_v_hack(pairs_from_pool(modes=known))
|
||
# CONFOUND (both): teacher pool MUST be `known`-only, else we TEACH `held`
|
||
# while measuring its suppression (back-door leakage).
|
||
train(intervention="route", V_hack=V, train_modes=MODES, teacher_pool=pool(known))
|
||
# PASS, PER-MODE (not global avg): hack on `held` ↓ while its pass stays flat.
|
||
# A global ±10pp pass band hides a per-mode pass collapse = capability damage.
|
||
assert hack_rate(held) ↓ and pass_rate(held) ≈ pass_rate_vanilla(held)
|
||
# ...AND real V beats random_V on `held` (else it's generic conservatism).
|
||
assert suppression(real_V, held) > suppression(random_V, held)
|
||
|
||
# CONFOUNDS that make "held-out ↓" weak evidence (both families):
|
||
# capability damage · shared surface features (all 4 modes = "fake a success
|
||
# signal", not independent) · selection contamination (known-detector false-
|
||
# positives on held) · teacher contamination / cross-mode coupling under joint train.
|
||
|
||
# ── Health checks (gate validity; NOT evidence of efficacy) ────────────
|
||
# All cin/cout use ‖relu(V@g)‖/‖g‖ (relu-before-agg, see 03): the hack-ward
|
||
# fraction, matching what the one_sided gate removes. A signed aggregate would let
|
||
# anti-hack axes cancel and mis-state both cin and cout.
|
||
# cin_s (NOT just cin_t>cin_s) : the live STUDENT-gradient overlap. If cin_s≈0
|
||
# throughout, V misses the live gradient → method is a NO-OP whatever cin_t
|
||
# does. Want cin_s>0 AND falling as hack rate falls. (deepseek top fix)
|
||
# control cin on teacher-CORRECT vs teacher-HACK rollouts: equal ⇒ V tracks the
|
||
# teacher DISTRIBUTION (length/reward/syntax), not hack-ness. (both)
|
||
# cout ≈ 0 : with relu, a REAL residual-hack-ward-leak check under
|
||
# one_sided (still a tautology under no_gate). cf. cout on the UPDATE Δδ (not g)
|
||
# to catch the Adam-preconditioner leak — see 03 / failure mode 4.
|
||
# cin_t decay/refresh: tracks a labeled-pair artifact; mild evidence at best.
|
||
# δS=0 ⇒ bit-identical: free-ref-model + measure_only arm are honest.
|
||
|
||
# ── What would FALSIFY / where it could be fooling us ──────────────────
|
||
# 1. erase ≈ vanilla at matched pass → V doesn't overlap the live GRPO subspace
|
||
# (central unproven hope); labeled-pair gradient ≠ unlabeled GRPO gradient.
|
||
# 2. erase ≈ random_V/shuffled_V/nonhack_V → generic regularization, not hack
|
||
# removal. THE control that rules out the boring story.
|
||
# 3. TEACHER-POOL IMITATION (both, deepest leak): erase only "works" by blocking
|
||
# advantage-weighted imitation of cached teacher completions. Test: does erase
|
||
# still suppress with NO teacher pool (pure student GRPO)?
|
||
# 4. AdamW MOMENTUM BYPASS: we project g but the optimizer steps on moment m, which
|
||
# banks hack-ward mass. cout≈0 while the update is still hack-ward → silent no-op.
|
||
# 5. held-out ↓ only via capability damage → enforce matched pass PER held-out mode.
|
||
# 6. confound: a "more pairs" win that is really k or τ_axis → one-knob sweeps.
|
||
# 7. OUTCOME-FLAG DEFLATION: exploited = passed ∧ ¬gt_correct falls when passed
|
||
# falls; report P(exploit | attempt) via the code-structure detector (E flag).
|
||
# 8. rotation: early hack drop is temporary; model relearns hacks orthogonal to V.
|
||
|
||
# ── PRIORITY (reviewers + blog "what I'd want to see") ─────────────────
|
||
# The head-to-head exists (Table 1, n=2). The cheapest things that would change
|
||
# belief, in order:
|
||
# 1. n=3 matched seeds (blog #1; pueue queued). At n=2, SEM is not evaluable — the
|
||
# spec's SEM falsification clause can't even be checked yet.
|
||
# 2. random-V control, ONE extra run paired with the existing erase run. A lone
|
||
# erase-beats-vanilla win can't rule out projection-as-regularization; random-V
|
||
# can. Cheapest insurance. (shuffled-V / nonhack-V later.)
|
||
# 3. cos null baseline (blog #3): two random unit vecs in r-dim with k kept axes
|
||
# have E[cin] ≈ sqrt(k/r). "+0.28" only means something above THAT. Compute it.
|
||
# 4. Direct overlap audit: ‖relu(V@g)‖/‖g‖ energy on the live STUDENT grad; correlate
|
||
# with per-batch exploited fraction; student vs teacher terms separately.
|
||
# 5. DROP route until erase is proven (route = erase + quarantine + deploy-ablation
|
||
# shock). Then weak-detector leave-one-out.
|
||
# NB the headline is against ONE dominant mechanism: the teacher pool is degenerate,
|
||
# ~96% of rollouts fire just two correlated signatures (E=loophole_used, C=arbitrary_
|
||
# pass). Cross-mechanism (G2/G3) is the load-bearing generalization test, not these.
|