Living log. Context: Part 5 method map, Part 6 five bets.

The bet — and the correction that motivated it

“Anti-collapse” (VICReg, uniformity, structural entropy) is a JEPA property. A pure JEPA has no reward/value loss, so anti-collapse is the only thing preventing latent collapse — load-bearing by construction. But TD-MPC2 is not a pure JEPA: its value/reward losses already anchor the latent. So our earlier DMControl “anti-collapse” study actually measured redundancy (a relational regularizer fighting an already-anchored latent), not JEPA anti-collapse. To ask the JEPA question honestly you strip the value loss. That is Thread D.

Four arms: D1 SE-arm on TD-MPC2 (a redundancy datapoint, correctly scoped); D2 a pure self-predictive JEPA on DMControl state; D2-ext firming D2 at fixed λ across more tasks; D3 pixels, where the information term should finally matter.

Status board

arm state verdict
D2 — pure JEPA on state ✅ done premise falsified — no collapse
D2-ext — fixed-λ, 3 tasks + online ✅ done reversal FIRMED
D3 — pixel JEPA ✅ done NULL — same picture as state
D1 — SE-arm on TD-MPC2 (redundancy) ✅ done SE hurts like uniformity; SE+unif no synergy
open cell — narrow/on-policy data collapse? ✅ done NULL — narrow data doesn’t collapse pure JEPA
final cell — is closed-loop feedback the trigger? ✅ done PARTIAL — a soft amplifier, not a clean trigger

Net: Thread D is fully resolved. It reverses the Part-5 anti-collapse story; the collapse regime is narrower than any data-distribution story; and the one setting that did collapse (closed-loop nav) is only softly explained by the loop — the latent is chronically low-rank there regardless, and, either way, representation collapse stays decoupled from control (success is identical with or without a dead latent).

The finding (D2 + D2-ext + D3)

A pure self-predictive JEPA does not collapse on DMControl — state or pixels. The none arm (zero anti-collapse) has the best readouts; adding an anti-collapse term ranges from neutral to actively harmful. The load-bearing ingredient is the predictor + EMA-target (BYOL) asymmetry, not any explicit repulsion — and that asymmetry is present in both TD-MPC2 and a pure JEPA. LeCun’s collapse concern does not materialize even in the high-dim pixel regime his argument targets. The “downstream-dependent taxonomy” (uniformity helps geometric readouts, hurts value readouts) was specific to the narrow nav-collapse regime, not a general law.

Progress log

2026-07-01 — the final cell: closed-loop feedback is a soft amplifier, not a clean trigger

The one setting that ever collapsed was the closed-loop online nav H-JEPA. Direct controlled A/B on the same architecture (matched 6094 updates, (n{=}3)), varying only the data-collection policy: online (closed-loop, policy co-evolves with the representation) vs offline (fixed random-policy buffer, loop broken).

  • Robust signals (mid-training eff-rank floor + a (z\to)state readout-R²): online hits a dead latent (eff-rank ≈ (10^{-7})) on 3/3 seeds with readout R² ≈ 0.19; offline on only 1/3, keeping 2× more decodable structure (R² ≈ 0.41). Breaking the loop mitigates collapse — so closed-loop feedback is a real driver.
  • But it’s not a clean trigger, and the metric fights itself. The final-eval eff-rank (noisy, one online seed bounces to 1.66) actually reads online 0.55 > offline 0.42 — backwards from the trajectory floor. Both arms are chronically low-rank on the 4-dim nav task (offline nowhere near the eff-rank 10–22 of broad DMControl), so much of the low rank is architecture/task, not the loop. The strong binary hypothesis is a null; the weak “closed-loop deepens the transient collapse” gets partial, fragile support.
  • And it doesn’t matter for control: task success is identical (~0.64) with or without a dead latent — representation health is decoupled from performance, the same thing every nav probe has found.

So the honest close on Thread D: the nav collapse is not cleanly explained by any single lever — closed-loop feedback is at most a soft amplifier of a latent that is low-rank anyway, and the collapse is invisible to the policy. (Caveats: offline used a random, not frozen-expert, buffer; a ≥300k budget with more seeds would sharpen whether the residual rank-loss is coverage or architecture.) That is the last open question in the thread, closed.

2026-07-01 — D1 done + the open cell closed: the collapse regime is narrower than “on-policy data”

Two harvests land together and finish the thread.

D1 — SE arm on TD-MPC2 (the correctly-scoped redundancy datapoint), n=3, L16 collapse config. Return-AUC vs the known default/uniformity baselines:

task default uniformity SE SE+unif
CheetahRun 58.9 36.6 23.4 29.0
WalkerWalk 293.8 89.9 110.3 62.5
FingerSpin 249.4 171.8 214.4 63.8

SE hurts value-based control just like uniformity (default > SE on all three), and SE + uniformity is worse than either alone — no synergy. Importantly SE is not catastrophically over-weighted here (FingerSpin SE 214 ≈ default 249, not nuked) — so the D2 grad-match artifact does not apply on a value-anchored latent, exactly as D2 predicted (‖∇se‖≈‖∇unif‖ there). Confirms the redundancy law: on a value-sufficient latent the right anti-collapse is nothing extra; any relational term (uniformity, SE, or both) hurts.

Open cell — does narrow / on-policy data collapse a pure JEPA where broad data doesn’t? NULL (WalkerWalk, n=2, partial). A distribution-width sweep (broad-random → broad-smooth → on-policy(ARS) → narrow top-return 2–25%) on the same none arm. eff_rank never approaches 0 (stays 10–22); geometric decodability holds (0.5–0.83). Value-R² does fall as the buffer narrows (0.11 → −0.05), but that mirrors the narrow buffers’ intrinsically low value variance (raw-obs value-R² is also ~0.04–0.11 there) — a property of the data, not encoder collapse. So the nav H-JEPA collapse (eff_rank≈0) is not reproduced by data-distribution width alone. (Honest caveats: two pipeline bugs — ReacherEasy width buffers never generated, broad-random train arm hit a shell word-split; “on-policy” here is an ARS refit, not a true closed-loop value-optimizing agent. That last, genuinely-on-policy closed-loop cell is the only condition still untested.)

Thread D, closed. Pure self-predictive JEPA does not collapse on broad DMControl (state or pixels) or on narrow/on-policy-ARS data; anti-collapse is neutral-to-harmful throughout; on a value-anchored TD-MPC2 latent all relational terms (uniformity, SE, SE+unif) hurt. The one setting that did collapse — the nav closed-loop online H-JEPA — is not explained by data width, so the true collapse trigger is the value/goal closed loop itself, not the breadth of the data. That is the honest remaining open question.

2026-07-01 — D3 (pixels): NULL — anti-collapse not load-bearing on pixels either

Rendered 64×64×3 pixel DMControl (via mujoco_playground MJX dynamics + an EGL renderer, since dm_control’s loader is broken against mujoco 3.8), DrQ-style 4-conv CNN encoder into the D2 harness, latent self-prediction only, frozen-encoder ridge probes. 2 tasks × 4 fixed-λ arms × n=3 (30 runs).

  • Pure JEPA does not collapse on pixels: none reaches eff-rank 33–34/64 and the best/tied-best readouts on both tasks (Walker geom 0.314 / value 0.216; Cheetah geom 0.421 / value 0.168). No arm collapsed.
  • Anti-collapse rescues nothing: uniformity maximizes eff-rank (61/64) but gives the worst readouts (Walker 0.175/0.109) — the same inverse eff-rank↔readout pattern as state. vicreg ≈ neutral; SE mildly hurts value. Order neutral→harmful is identical to the state result.
  • The only shift vs state is lower absolute geom-R² (0.31 vs 0.77) — decoding qpos from a single frame (no velocity, no frame-stack) is simply harder. That is task difficulty, not collapse.

Caveats (honest): single frame caps absolute value-R²; fixed-λ only; small CNN, 12k steps, random buffer; strict dimensional collapse never observed on any arm. b3060b:exp/proposal_D3_pixel_jepa/.

2026-07-02 — D2-ext: reversal FIRMED (3 tasks, fixed λ, n=3)

Firmed D2 across WalkerWalk/CheetahRun/ReacherEasy at fixed λ (no grad-matching) + a WalkerWalk online probe. (1) Pure JEPA doesn’t collapse — none best-or-tied on 6/8 readouts; ReacherEasy none value-R² 0.257 ≈ 7× raw-obs; WalkerWalk-online none beats raw-obs on both readouts (latent enrichment). (2) No downstream-dependent taxonomy — uniformity hurts both readouts everywhere; geom and value move together. (3) Grad-match = artifact confirmed — at fixed λ nothing nukes training. Two honest self-corrections to D2’s own claims: none is best-or-tied (not strictly dominant), and vicreg is the most-neutral regularizer, not SE. Only open cell: truly reward-optimizing on-policy data (the nav regime) — the smooth-correlated probe did not induce collapse. b3060b:exp/proposal_D2_pure_jepa/ext/.

2026-07-02 — D2: the pure-JEPA test REVERSES the story (WalkerWalk, n=3)

Correctly-scoped JEPA: encoder + jumpy predictor + EMA target, no reward/value/policy loss; frozen-encoder ridge probes for geometric and value(RTG) readouts (differentiable-2D-SE validated vs selib to 3e-7). Three surprises: (1) premise falsified — the none arm has the highest decodability (geom 0.795, value 0.304, above raw-obs), so collapse is prevented by the BYOL predictor+EMA asymmetry, not the value anchor (my own “value loss prevents collapse” correction was itself wrong). (2) The downstream-dependent taxonomy does not hold on a pure JEPA — uniformity hurts both readouts while raising eff-rank 5→40. (3) SE ≠ uniformity, and grad-norm-matching is a trap: it inflated SE’s weight ~74× and destroyed the readouts — a catastrophic over-weighting artifact, not intrinsic to SE. This flagged that D1 (SE-arm on TD-MPC2, grad-matched) must be re-checked with a fixed-λ control before any “SE hurts value-control” claim. b3060b:exp/proposal_D2_pure_jepa/.

Open / next

  • D1 (SE-arm on TD-MPC2) still grinding on b3060 (~4/18) as a redundancy datapoint — harvest and re-read through the fixed-λ lens (its grad-matched setup may over-weight SE exactly as D2 showed).
  • One open cell: does a pure JEPA collapse under truly reward-optimizing on-policy data (the nav regime)? The smooth-policy probe didn’t trigger it; this is the honest remaining question for the JEPA story.