Correction / addendum. Parts 42–48 framed a behavioral abstraction as beating PPO on several tasks. A subsequent matched-control campaign showed those wins were measured against an under-budgeted benchmark PPO. Against a budget-matched vanilla PPO, the abstraction wins only on sample-efficiency, never on the asymptotic ceiling. Per this project’s verification discipline we do not delete or silently edit the earlier posts — we leave them standing and correct the record here, with a one-line pointer added to the top of each affected part.

What the earlier parts claimed

  • Part 42 — abstraction-as-curriculum (lean bootstrap then release) “beats PPO” on PandaOpenCabinet (~2.5× sample-efficiency at matched success).
  • Part 43 — residual-on-TAMP “beats PPO” on PandaOpenCabinet (~9–18× sample-efficiency, more stable than the curriculum).
  • Part 44 — the dual-protocol bookkeeping table for the beat-PPO cells.
  • Part 47 — the honest negative: the prior backfires on AcrobotSwingup (already a negative; restated here for completeness).
  • Part 48 — the unified DMC table, which carried the “abstraction beats” cells (Pendulum) into one place.

The common thread: the PPO bar these were compared against was not budget-matched. On OpenCabinet the cited PPO reached its success at 29.5M env-steps; on the DMC tasks the PPO column was “PPO’s best at a far larger budget” (50–285M steps) rather than a PPO trained under the same conditions/budget as the abstraction. That makes the sample-efficiency comparison informative but makes any asymptotic / ceiling “beat” claim unsupported.

The correction: budget-matched results

Source: wm-redundancy-paper/SYNTHESIS_beat_ppo.md and the per-task verdict JSONs it cites (deterministic, multi-seed, disk-backed). The control that changed the picture is a budget-matched vanilla PPO — the same paradigm (controller + residual trained by PPO) vs a plain PPO given the same budget.

regime asymptotic return (ceiling) sample-efficiency
Reaching, fit member (ReacherHard) tie (residual 981 vs vanilla 977) residual wins (~3×: solved ~1M vs ~9.8M)
Reaching, unfit member (FingerTurnHard) vanilla PPO wins (952 vs 923) no advantage
Locomotion (all 5 tasks, n=5) vanilla PPO wins all 5 (e.g. Cheetah 914 vs 767; 3/5 CI-separated) no advantage
Sparse swing-up (CartpoleSwingupSparse) tie (both solve) residual ~2× faster
OpenCabinet (hard manipulation) tie (0.987 vs 0.988) residual wins ~7×

Pareto experiment: NULL. No TD-MPC2-plus-abstraction variant Pareto-dominates PPO across both return/sample-efficiency and wall-clock; adding temporal abstraction (jumpy) was null-to-negative. The sample-efficiency ↔ wall-clock tradeoff is architectural, not a tuning gap.

What survives, and what does not

  • Does NOT survive: any claim that the behavioral abstraction beats PPO on the ceiling / asymptotic return. Against a budget-matched baseline it never raises the ceiling — it ties where the prior fits and loses where it does not (FingerTurnHard; all five locomotion tasks).
  • Survives (qualified): the abstraction is a sample-efficiency lever, and only where the prior captures the task’s control-relevant substructure (actuated-DOF ≈ goal-DOF). The two concrete survivors are ReacherHard (~3×) and OpenCabinet (~7×) sample-efficiency at matched final performance. CartpoleSwingupSparse is a softer ~2× at a tie.
  • Already honest, unchanged: Part 47’s AcrobotSwingup result — a weak/ill-fitting prior is an anchor, slower than scratch. The matched-control campaign is consistent with it.

The honest conclusion (revised)

Abstraction is a sample-efficiency lever, not a better ceiling. A behavioral prior can lower the statistical / optimization cost of reaching a competent policy when the baseline is optimization-limited and the prior fits the task’s control structure — that is a real and useful win on env-step-scarce settings (real robots, costly sims). But it does not raise the representational ceiling: a budget-matched strong baseline already finds a value-sufficient solution, and where the prior does not fit, wiring it into the loop is dead weight or an anchor.

This is a narrower claim than Parts 42–48 made, and we are not over-correcting in the other direction either: the sample-efficiency wins on ReacherHard and OpenCabinet are real, multi-seed, and disk-backed. What changed is only the axis on which “beats PPO” is true.

Provenance

  • Corrected numbers: wm-redundancy-paper/SYNTHESIS_beat_ppo.md (synthesis of the 2026-06 matched-control campaign) and the per-task verdict JSONs it cites.
  • Affected posts (pointer added at top, original bodies unchanged): Parts 42, 43, 44, 47, 48.
  • This post adds to — does not replace — the public record. The earlier parts stay up for provenance; read this correction alongside them.