# Results, organized by the question each run answers Deploy-eval is the headline metric: knob-off forward on the recency-clean held-out TEST set (ids>=3243, base solve ~0.1, n=119), single-mode `run_tests` env, Qwen3-4B. Regenerate the table with `just results-deploy` (scripts/results_deploy.py, auto-discovers every `out/runs/*/deploy_test.json`); `just results` gives the live training-hack table. OLD eval1 results (training-hack metric, the Q1-Q13 mechanism/basis/refresh studies on the pre-recency-clean eval) are archived in [results_eval1_archive.md](results_eval1_archive.md). ## How to read this - **Headline = solve_deploy - hack_deploy** (both alone are gameable; the gap is the honest objective: solve the task without learning the cheat). Higher is better. - **hack_deploy / solve_deploy** = knob-off deploy eval (quarantine deleted for routeV; the trained model for vanilla) on test n=119. **hack_train / solve_train** = last-5-step student rates during training (converged regime). - **All rows n=1** (single seed 43) unless noted; treat gaps <=0.03 as noise. - Watch the **pool/pairs confound** across rows (see `argv` / `train_set`); only same-axis pairs are A/B-comparable (called out in the answer). --- ## Q14. 🥇 routeV deploy on the recency-clean eval2 test set (the current headline) Everything above (Q1-Q13) is on the OLD eval. Q12's route2 numbers used n=64 prompts before the recency-clean fix; the env is now single-mode `run_tests` and the held-out test set is recency-clean (ids>=3243, base solve ~0.1). This is the corrected substrate. All rows: seed 43, 60 steps, deploy = knob-off forward on test n=119. Headline = solve_deploy - hack_deploy. Note the pool/pairs confound across rows (see `argv`); the only single-axis A/Bs are called out in the answer. | arm | pairs | gran | hack ↓ | solve ↑ | headline | | :-- | :-- | :-- | --: | --: | --: | | routeV per-token | prog_wide | per-token | 0.042 | 0.143 | +0.101 | | routeV authored | authored | per-rollout | 0.076 | 0.118 | +0.042 | | routeV prog_wide | prog_wide | per-rollout | 0.101 | 0.126 | +0.025 | | routeV random-V | prog_wide (Haar dir) | per-rollout | 0.101 | 0.109 | +0.008 | | vanilla GRPO | -- | -- | running (job 16) | | | | routeV act_vote | authored | per-rollout (global vote) | queued (19) | | | | routeV LoRA-B | authored | per-rollout | queued (20) | | | | routeV random-V | authored (Haar dir) | per-rollout | queued (21) | | | **Answer: three single-axis reads already hold; the suppression magnitude waits on vanilla.** - *Direction doesn't matter at per-rollout (H2 absorption, on eval2):* real-V (prog_wide, 0.101) == random-V (prog_wide, 0.101). Replicates the old-eval H2 result on the clean set. The open question is whether AUTHORED direction matters (job 21 random-V-authored vs job 15) -- queued. - *Pairs matter:* authored per-rollout 0.076 < prog_wide per-rollout 0.101 (clean A/B -- same granularity, same dense pool, differ only in pairs). Authored helps even though real-vs-random didn't, so the gain is the pair CONTENT, not direction sharpness. - *Granularity matters most:* per-token 0.042 < per-rollout 0.101 (both prog_wide). The arm the per-token ablation -- lowest deploy hack AND highest solve (0.143) of any completed run. All single seed (n=1), so treat <=0.03 gaps as noise. NOT yet interpretable as suppression until vanilla (job 16, running): if vanilla deploy hack is ~0.10 then even per-token's 0.042 is only a modest cut over base; if vanilla is high (>0.3 as on the old eval), all routeV arms suppress strongly. Pairs separability (orthogonal, job 17): authored_all p@10=0.70 beats prog_wide 0.20 (`out/diag/pairs_compare.csv`). Training-`rout` note (not deploy): grad-cosine routing cliffs (0.63@step6 -> 0.09@step20, GRPO advantage flattening); act_vote sustains late (0.88@step17) by gating on activations -- see RESEARCH_JOURNAL 2026-06-08. Whether that converts to deploy suppression is what job 19 tests. ## Dynamics note (sizing the convergence test) Per-step trajectories (mix=0.125 g8, seed 41): `hack_s` rises 0→~0.6-0.75 and **plateaus by step ~13-16**; `gt_s` (solve) stays **noisy-flat at ~0.1-0.5 the whole run, it never climbs**. The attractor in this surrogate regime is full *hack*, not full solve — so "run until full solve" has no target. The convergence question is therefore: once vanilla hack plateaus (~step 15), does projected stay below it or catch up? A 60-step run (~2.2h at g8) sees 3x past the plateau; a 1000-step run (~36h) is wasteful. ## Open / queued (no result yet) - **convergence at ≥3 seeds (#121)**: the n=1 seed-42 run (Q11) shows the gap closing by step 60, but that could be a seed-42 high-hack draw. Need 2+ more seeds before concluding the suppression erodes vs survives. - **pairset content at ≥3 seeds (#122)**: Q10's mechanism>framing>placebo ordering is n=1 per row; replicate `prog_wide` and the placebo on 2+ seeds. - **route arm at scale (#182)**: running; validates routing's ablated-eval hack