Mid-campaign, the right question arrived: “Are we wrong at the very beginning? Have we ever compared our JAX reimplementation of TD-MPC2 with the original?” The answer was no — we never had. Every result in Parts 1–15, both papers, the whole Hopper critique — all of it runs on a from-scratch JAX/MJX reimplementation whose fidelity to Hansen’s TD-MPC2 we had sanity-checked qualitatively but never audited. This post is the audit.

How the question surfaced

While preparing the P2 experiment (disable MPPI during data collection to test “planning-as-exploration”), we read our own collection loop before patching it — and found there was nothing to disable. Our implementation collects data with π + Gaussian noise in every branch; the MPPI planner never touches collection. An optional MPC-distillation loss exists but is default-off and was never enabled in any run. Canonical TD-MPC2, by contrast, acts with the planner during environment interaction — planner-collected data is part of the published method.

So the P2 hypothesis (“MPPI acts as a structured explorer during collection”) was falsified by construction in our stack — every number we have, including the flagship HopperHop win, is planner-free training. That is both a discovery and a problem: a discovery because it shows planner-collection is unnecessary for the Hop phenomenon; a problem because our results were being phrased as results about TD-MPC2 the published method.

The full deviation list (as audited)

  1. Data collection: ours = π + annealed Gaussian noise (0.3) + random warmup; canonical = MPPI planner actions.
  2. Actor objective: ours = fully deterministic — maximize \(\min_2 Q(z, \tanh(\mu_\pi(z)))\) with a RunningScale normalizer, no entropy term, no action sampling; canonical = sampled action + a very small entropy bonus (~1e-4) — functionally near-deterministic, but not literally.
  3. Physics backend: ours = mujoco_playground / MJX; canonical = dm_control / MuJoCo. Same reward definitions, different numerics — absolute returns are not exactly commensurable.
  4. MPPI budget: ours = 2048 samples, horizon 3; canonical = 512 samples, horizon 3.
  5. (Neutral) an MPC-distillation loss exists in our code but is default-off and unused.

V1 — parity against the official published results

The TD-MPC2 repo publishes per-task curves (3 seeds, 4M steps, dm_control). Overlaying our numbers:

task official TD-MPC2 final @4M ours @5M gap official cross-200 ours
hopper-hop 449 (373/380/594) ~420±113 (mppi ~571) ≈0% 0.2–0.4M ~1M
cheetah-run 896 855 −5% 0.1M —
walker-run 877 727 −17% 0.1M —
acrobot-swingup 663 511 −23% 0.1–0.2M —

And the reference baselines the repo ships alongside:

  • Official SAC on hopper-hop: finals 0 / 246 / 105 (only 1 of 3 seeds ever crosses 200). Our custom SAC v1 (finals 76 / 23 / 101, 0/3 crossing) sits inside the canonical band — the “SAC fails on HopperHop” leg of our P1 attribution is not an implementation artifact; reference SAC exhibits the same standing-trap phenotype.
  • Official TDMPC-v1 on hopper-hop: 2 / 577 / 1 — the task is seed-brutal for everyone.

Three verdicts.

  1. The two pillars stand at canonical levels. Our variant reproduces both the TD-MPC2 hopper-hop level (parity) and SAC’s hopper-hop failure (parity). The Part-12 critique was not built on a broken reimplementation.
  2. The deficits land exactly where our own theory says they should. Our variant is weaker than official precisely on the tasks where we measured the world model to be load-bearing: the official-minus-ours gap ordering (Hop ≈0% < Cheetah 5% < Walker 17% < Acrobot 23%) tracks our measured WM-load-bearing ordering (Hop removable < Walker −7.5% < Cheetah ~−19% < Acrobot −44%). If planner-collection (which rolls the world model) helps most where accurate rollouts matter, this is exactly the signature you’d expect — canonical data cross-validating the task-conditional world-model story from the outside. (Honest confound: the MJX backend differs too; we cannot fully separate the two from published curves alone.)
  3. Early-phase speed does not transfer. Official crossings are uniformly faster (0.1–0.4M vs our ~1M) — so sample-efficiency comparisons are only made within our stack, never across stacks.

The one claim genuinely at risk — and the experiment running right now

“The consistency loss is removable on HopperHop (n=8)” was measured in a stack where the planner never touches training. In canonical TD-MPC2 the planner collects the data by rolling the world model — so removing the consistency loss there could also degrade collection in a way our stack cannot express. If that matters, canonical TD-MPC2’s world model might not be removable on Hop, and Part 12’s headline would need a variant-scoped rewrite.

V2 (running): we added an MPPI_COLLECT=1 gate that makes our stack collect with the planner (512 samples, the canonical count), and launched {full, consistency-stripped} × planner-collection on HopperHop (n=2 each, 2.5M). If the stripped model still trains to full under planner-collection, removability holds beyond the deviation and the critique stands as stated. If it collapses, we will have found precisely where the claim’s boundary is — and Part 12 gets rescoped to policy-collection variants, honestly labeled. Either outcome is informative; the verdict lands within a day and will be appended here.

What changes in our papers regardless

  • Every external-facing “TD-MPC2” becomes “our TD-MPC2 variant (policy-collection; deviations listed in setup)” until/unless V2 licenses the stronger phrasing.
  • The deviation list above goes into both papers’ setup sections verbatim.
  • Internally-controlled ablations (the sufficiency grids, VBN curves, reweighting/bisim nulls) are unaffected — they compare arms within one stack and were always statements about this architecture family.
  • One deviation turned into a finding: planner-free training reproduces the canonical hopper-hop level — evidence that planner-collection is unnecessary for the flagship result, which is itself a contribution of the critique.

The lesson, stated plainly: when your research program runs on a reimplementation, the parity audit is not optional due diligence — it is an experiment, and (as here) it can return results.

Update (2026-07-11): the V2 verdict — removability survives planner-collection

The experiment landed, and the answer is unambiguous. With the MPPI_COLLECT=1 gate making our stack collect data canonical-style (the planner acts during environment interaction, 512 samples):

arm (HopperHop, 2.5M, n=2) seed 50 seed 51 mean
full world model + planner-collection 467.8 462.2 465.0
consistency-stripped + planner-collection 451.8 479.8 465.8

Stripped equals full to within noise (+0.2%). The consistency loss is removable on HopperHop even when the planner collects the data — including in the stripped arm, where MPPI is rolling an untrained dynamics network and scoring candidate actions purely through the reward and value heads. That is simultaneously (a) the discharge of the one claim this audit put at risk — Part 12’s critique holds beyond the implementation deviation, and (b) the strongest evidence yet for the execution-simple account: on Hop, what the planner contributes is value-guided action scoring, not rollout fidelity. The papers can now state the removability result as holding under both policy-collection (n=8) and planner-collection (n=2). The natural reviewer follow-up — the same planner-collection contrast on WalkerRun, where the world model is load-bearing — is queued.

Update 2 (2026-07-11): the Walker mirror-image — a complete double dissociation

The pre-registered follow-up ran: the same planner-collection gate on WalkerRun, where the world model is load-bearing. Prediction: the stripped model should degrade there. Result (2.5M, n=2/arm):

task full + planner-collection stripped + planner-collection Δ
HopperHop 465.0 465.8 +0.2% — removable
WalkerRun 721.9 (758/686) 605.4 (601/610) −16.1% — non-overlapping

Confirmed, cleanly. Two further reads: the planner-collection gap on Walker (−16% at 2.5M) is larger than the policy-collection gap (−7.5% at 5M) — planner-collection amplifies the world model’s importance exactly where rollout quality matters, because the planner collects by rolling the model. And the full model under planner-collection reaches at 2.5M what policy-collection needs 5M for (~722 vs the 708–727 band) — planner-collection roughly doubles Walker sample-efficiency in our stack, which retroactively explains the V1 deficit pattern (official, planner-collecting TD-MPC2 beats our policy-collecting variant precisely on the load-bearing tasks). Together, V2 + V2W form the cleanest mechanistic statement of the program: the world model’s value is task-conditional, and collection mode modulates it in the predicted direction at both ends.

Update 3 (07-12) — the n=4 resolution, and what the “bimodality” really was. Seed 52 briefly complicated the story: its full arm finished at 455, far below s50/51 (758/686), while its stripped arms stayed tight — raising the possibility that the full model under planner-collection is bimodal across seeds. The seed-53 resolving pair settled it: full 744.7, stripped 558.3 (finals at 2.5M), so 3 of 4 full seeds cluster at 686–758 and s52 is the lone outlier. Better, watching s53’s eval trajectory live revealed the mechanism: the full arm’s evals swing ~250 points within a single late-training window (680 → 715 → 676 → 501 → 696 → 744 over 2.1–2.5M) while the stripped arm moves ~30 points (539–568). The dissociation at n=4: full median 715.5 vs stripped 600.5 (−15.4%; means −10.4%). So the refined claim is variance-aware: on WalkerRun under planner-collection, the world model buys a higher-performance but higher-variance eval regime, and the stripped model trades ~115 points of median for stability. s52’s 455 was almost certainly an unlucky final-eval draw from the volatile regime, not a distinct mode. Hop’s ±0 (n=3, tight) stands unchanged — the double dissociation is now n=4 on the Walker side.

Update 4 (07-13) — the third task inverts the story, exactly as a pre-registration should catch. With Hop and Walker in hand we pre-registered the natural generalization: planner-collection amplifies the world model’s importance, so stripped-Cheetah should degrade ≥15% (kill: <8%). It did neither — it inverted. Across both seeds the full model’s evals destabilize under planner-collection (swinging 141–585 within the last 250k steps) while the stripped model holds a tight ~460–555 band; on last-6-eval medians the stripped model is ~40% above full. Walker meanwhile strengthened to n=5 (full median 739 vs stripped 601, −18.7%), and Hop stayed removable at n=4 (436 vs 468). The three-task table is now: removable (Hop) / load-bearing-but-volatile (Walker) / actively destabilizing (Cheetah) — the collection-mode × world-model interaction is task-dependent and non-monotone in how much the value function needs the latent (the VSB grid’s ordering). That is a sharper claim than “amplification,” and it is what the planner-rolls-the-model mechanism actually predicts once the model’s rollout errors compound differently per dynamics class. Cheetah n=5 pairs are running to nail the inversion.