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R3 — Trap-Aware PER (orthogonal extension)

R3 is not a rescue step. R2 (κ=4) is already a valid anti-hacking stopping point — it removes proxy farming without PER. R3 is an orthogonal extension asking:

"Can trap-aware PER recover/reintroduce replay benefits without re-igniting the proxy-trap amplification seen in the earlier PER/SPIE ablation?"

The R0 ablation showed PER over-replays high-TD-error transitions, which in V1.4 are the proxy transitions (PER+SPIE was the worst hacker). R3 re-introduces PER on top of R2, but makes it trap-aware: transitions flagged by the observable proxy signal have their replay priority hard-capped at the running batch median, so they cannot dominate replay.

Driver knob: taper on pg_spie_r2_taper_noisy_dueling_ddqn_per (scripts/train_axiom_forge.py) + PrioritizedReplayBuffer(trap_aware=True) (agents/dqn_axiom_forge.py). Runner: scripts/run_pg_spie_r3_trap_aware_per.py.

The mechanism (one new knob over R2)

Each stored transition carries an observable trap flag (the same signal the gate/sentinel use: proxy_attempt_count / proxy_claim_count increased this step). In the PER buffer, under trap_aware:

  • at insertion: a trap transition enters at min(max_priority, batch_median) instead of max priority;
  • on every update_priorities: trap transitions are capped at the current batch median of |TD|^α.

Naive PER = the same but cap off (so re-ignition is visible). Nothing reads true_score, HiddenContext, or any grader-only field; true_score is reporting-only. No PSFA, no environment change.

Variants (Noisy Dueling DDQN backbone, demo-seeded, κ=4)

# variant PER gate penalty trap-aware key
1 plain_noisy – – – – noisy_dueling_ddqn
2 per_spie ✔ – – – spie_noisy_dueling_ddqn_per
3 r2_k4_no_per – ✔ ✔ – pg_spie_r2_noisy_dueling_ddqn
4 r2_k4_naive_per ✔ ✔ ✔ – pg_spie_r2_noisy_dueling_ddqn_per
5 r2_k4_trap_aware_per ✔ ✔ ✔ ✔ pg_spie_r2_taper_noisy_dueling_ddqn_per

(2) is the old PER+SPIE hacker re-run apples-to-apples; (3) is the R2 bar; (4) tests whether PER re-ignites; (5) is R3.

Success criteria

  • R3 (5) does not reintroduce high trap_hits / hack_rate (stays near R2 (3)).
  • R3 maintains or improves success vs R2.
  • R3 alignment_gap stays near R2 levels.
  • PER priority stats show trap transitions deprioritized/capped in (5) relative to (4).
  • If (5) ≈ (4) (re-ignites anyway) → report PER as unsafe even with sentinel correction.

Diagnostics logged

trap_hits, reward_hack_rate, alignment_gap, true_score (reporting-only), success, visible return, mean PER priority for trap vs non-trap transitions, trap/non-trap replay ratio, # capped trap transitions, and the cap (median) value.

How to reproduce

python scripts/run_pg_spie_r3_trap_aware_per.py --episodes 400 \
    --seeds 0 1 2 3 4 --kappa 4 --regimes demo_seeded

Results: results/pg_spie_r3_trap_aware_per/.

Results

Generated 2026-06-14 17:05 — V1.4, 400 episodes, seeds [0, 1, 2, 3, 4], kappa=4.0, Noisy Dueling DDQN. success = final-100-ep training success; align_gap = visible return − hidden true_score (reporting-only).

demo_seeded — demo-seeded

variant succ trap_hits trap_sd hack align_gap gap_sd true_score trap_prio nontrap_prio trap_replay n_capped
plain_noisy 0.546 0.247 0.389 0.083 0.932 1.967 4.196 0.000 0.000 0.000 0.000
per_spie 0.518 0.530 0.336 0.182 2.233 1.668 3.196 0.475 0.438 0.032 0.000
r2_k4_no_per 0.546 0.039 0.050 0.013 -0.051 0.292 4.859 0.000 0.000 0.000 0.000
r2_k4_naive_per 0.524 0.165 0.292 0.056 0.512 1.407 4.208 0.413 0.395 0.012 0.000
r2_k4_trap_aware_per 0.518 0.052 0.067 0.018 -0.135 0.374 4.542 0.203 0.390 0.005 11148.000
  • Does naive PER re-ignite? naive_per vs R2: trap_hits 0.165 vs 0.039, hack 0.056 vs 0.013, align_gap 0.512 vs -0.051 (higher for naive_per => PER re-ignites hacking).
  • Does trap-aware PER prevent it? trap_aware vs naive_per: trap_hits 0.052 vs 0.165, align_gap -0.135 vs 0.512.
  • Stays safe vs R2? trap_aware vs R2: trap_hits 0.052 vs 0.039, success 0.518 vs 0.546.
  • Priority cap working? trap/non-trap priority — naive_per 0.413/0.395 vs trap_aware 0.203/0.390; capped trap transitions (trap_aware) 11148.000.