Keyboard shortcuts

Press ← or → to navigate between chapters

Press S or / to search in the book

Press ? to show this help

Press Esc to hide this help

12. Solver/Heun-gap work — screening (1-NFE student BANKED 2026-08-06; rig-ft diagnosis branch — next rung waits on rig data, #16)

Tag: one-step · idea #12 · index

The h1536 adaRMS Heun-gap collapse did NOT transfer to h1024-on-AR-trunk (measured −0.28 at 10→30, first_mae −0.46): sampler quality is back on the table for the best flow lineage. Arms: step-count sweeps, solver variants, consistency/distillation toward 1–2-step deployment decodes (the distillation leg pairs with idea 1).

  • Scoring note (2026-08-05 paired analysis): flow’s deficit is ~all late-horizon (crossover step 2, monotone to +1.2 @40 — post). Score solver arms per-step: a solver fixing only late-horizon costs nothing at first_mae; pooled-only scoring would misread it.

  • Literature (2026-08-05 slice): SnapFlow (arXiv:2604.05656) is the distillation-leg recipe to try first. Plug-and-play SELF-distillation for flow-matching VLAs — no external teacher: mixes standard flow-matching samples with consistency samples whose targets are two-step Euler shortcut velocities from the model’s own marginal predictions; zero-init target-time embedding switches velocity-estimation vs one-step modes in one network. Claimed: ~12 h on ONE GPU, no arch changes; π0.5-3B 1-NFE matches the 10-step teacher (98.75% vs 97.75% LIBERO, 274→83 ms); tested on SmolVLA-500M too (−8.3% MSE, 3.56× e2e) — the closest external analogue to our trunk+flow-expert protocol. Cheapest falsification here: distill flow-80k, score the panel at 1-NFE vs Heun-30 (band: within the σ_draw noise floor of 6.6232). Also pairs with #1 (a distilled 1-step model makes mean-of-N nearly free).

  • Literature (2026-08-06 slice, ~10:0xZ): the one-step fallback/ follow-on menu, banked while the SnapFlow run climbs to its 10k probe. If the @30k endpoint misses its band (or to extend a hit), three distinct objective families now have external evidence: (1) One-Step Flow Policy (2603 era, self-distillation w/o pretrained teacher) — self-consistency loss + self-guided regularization + warm start, 71.6% avg on 56 sim manipulation tasks at 1-NFE; nearest competitor recipe to SnapFlow’s. (2) MeanFlow-based one-step VLA (arXiv:2603.01469) — average-velocity (MeanFlow) objective, claims to eliminate the consistency constraint entirely (the constraint whose s=t divergence we are currently watching drift); 8.7× vs SmolVLA. A MeanFlow arm would be a genuinely different objective, not a SnapFlow re-tune — the right shape for a paired follow-up if consistency-style distillation is what misses. (3) “Let It Be Simple” (arXiv:2606.05737) — claims VLA is image-to-text-like (strong conditioning), so HIGH-NOISE TRAINING alone yields one-step decoding (95.6% LIBERO-Long, no distillation stage at all); their “irreducible velocity loss” framing + the ablation note (weakening the condition erases the one-step gain) ties directly to our conditioning stack (#11/Q3). Cheapest local probe of (3): score the TEACHER at 1-NFE (zero training — we may already have this number from the @10k/endpoint probe protocol runs) and read how much of the gap distillation actually closed vs what high-noise fine-tuning would have to. No new launch implied; feeds the SnapFlow results post’s discussion + the next pre-reg if the endpoint branch fires. Correction hooks (papers-page deep read 2026-08-07, page): MeanFlow-VLA’s 8.7× is speed-for-accuracy (78% vs SmolVLA’s 84.5% avg, loses 2/3 tasks; NFE=1 config-sensitive down to 49% in their own sweep); Let It Be Simple’s one-step win is chiefly state-carried (no-state ablation ~0% everywhere) and its α=4 schedule degrades 10-step decoding to 63.4% — a specialization, not a free win; its small-irreducible-loss theory retroactively explains our student’s draw collapse, and teacher-at-1-NFE stays the free schedule-vs-distillation decomposition read.

  • PRE-REGISTERED (2026-08-06 ~00:3xZ, pre-reg): SnapFlow self-distill of flow-80k — full recipe deep-read and frozen (α=0.5/λ=0.1 mix, sg two-step-Euler shortcut targets, zero-init φ_s target-time embedding, 30k steps LR 2.5e-5 cosine, trunk frozen, ~12–20 h 1×H100). Primary: full panel at 1-NFE vs 6.6232 (+max(3σ_draw, 0.15) band, σ_draw by finalization amendment from draws runs 3–5); deployment headline: mean-of-10@1-NFE vs the AR anchor 5.8026 at ~one-Heun-5-draw cost. Fills the local-GPU queue slot after the draws chain + fairness probe; pre-launch impl checklist (φ_s, --distill snapflow, 1-NFE eval switch, oracles) = CPU work items.

  • IMPLEMENTATION COMPLETE (2026-08-06 ~00:3x–01:0xZ session): all five pre-launch checklist items landed; the launch path is zero-CPU. φ_s config-flagged + checkpoint-compat (sanctioned additive warm start in the –init-from guard/loader); --distill snapflow (α/λ frozen in code, mean- and sum-form so chunked backward stays available); bijou.eval --target-time {t,zero} loud 1-NFE switch recorded through report/npz/banner; 10 new oracles (validation gate (a) also RUN on the real checkpoint: 6/6 forwards bit-exact, PASSED); launcher staged + recipe diff-verified through the real parse_args (50 teacher fields verbatim, 11 pre-registered deltas), chains gates (a)+(b) then training then the endpoint 1-NFE panels (1/5/10 draws). Gate (b) drift eval + @10k probe script wait on GPU only. check.py 201.

  • σ_draw FINALIZED (2026-08-06 ~05:5xZ, amendment): σ_draw = 0.0159 from the chain’s pooled mean-of-N curve (gaussian-bias family, held-out N=5 error 0.087%; CPU-only, oracled) — 3σ = 0.048 < 0.15, the floor binds: endpoint adopt-signal iff 1-NFE chunk_mae ≤ 6.7732. Verdict family-independent (even the a-priori-max pure-noise reading gives 0.040 < 0.045). Fairness-probe direct measurement supersedes if larger. The launch’s last CPU-side blocker is closed — SnapFlow waits only on a quiet local GPU. Direct measurement IN (2026-08-06 ~07:4xZ): σ_draw = 0.02367 supersedes the pin; 3σ = 0.071 < 0.15 → the floor still binds, adopt band ≤ 6.7732 UNCHANGED. SnapFlow queues behind the #18.2 flip eval (~09:2xZ boundary) on the local GPU.

  • RESULTS INSTRUMENT BANKED BEFORE THE DATA (2026-08-06 ~09:3xZ, 4d48120): fontaine/scripts/snapflow_results.py — every frozen read (probe kill 9.6755, adopt ≤ 6.7732, falsify > 7.1232, edge ≤ 1.9831, deploy ≤ 5.8026, per-step horizon read + v2 column), oracles (a)–(e) green on banked data only, strict semantics guards (1-NFE/euler/target_time/draws/index-keying/full-panel all asserted). Gap caught by banking early: the chained stage-4 endpoint evals dump no npz → per-step read had no data source; addendum eval_snapdistill_endpoint_1nfe_npz.sh staged (noise-key pinned index explicitly). Standing hold: the --noise-key default stays index until the chain’s endpoint evals run at 30k. Hold RELEASED — default flipped to stable 2026-08-06 ~15:5xZ (#18.2 follow-on, see item 2 in the deep-dive list).

  • Pointer reads closed (lit slice 2026-08-06): OFP (2603.12480) — from-scratch one-step self-distillation (self-consistency + self-guided regularization + warm-start from temporal action correlations); π0.5 one-step beats the 10-step teacher on RoboTwin 2.0 — the reserve recipe if SnapFlow misses expectation 2, and its warm-start trick touches #1’s noise structure. GoldenStart (2603.14245) — Q-guided VAE priors + entropy control for online RL distillation — screened out (needs Q-functions/rollouts; not our offline setting). Golden Ticket noise search banked in #1.

  • RESULTS (2026-08-06 15:2xZ, post): the 1-NFE student HOLDS the panel — single draw 5.6036 / 1.7039 at ONE expert eval (beats teacher Heun-30 6.6232 AND the AR anchor 5.8026); mean-of-10@1-NFE 5.3675/1.5927 edges the teacher’s mean-of-10 (5.365) at 1/30 the compute. Adopt signal fired (≤ 6.7732). v1 panel / index keying as registered (record-only, stated not hidden). This row is the deployment-latency headline the leaderboard’s compute column measures.

  • Rig fine-tune @4k (2026-08-06 ~18:1xZ, diagnosis): ship rule fired the DIAGNOSIS branch — no upload. Rig-holdout draws1 got worse (+0.09/+0.04); per the owner steer the next rung waits on a better rig dataset (#16). The student itself stays banked.

  • A fourth pole on the one-step axis banked (2026-08-09, async execution II, FASTER 2603.19199): one-step for the head of the chunk, many-step for the tail. A horizon-aware timestep schedule (per-action hit times, mixed-schedule fine-tune, no architecture change) finalizes action 0 after one flow step of N and streams it while the tail keeps refining — TTFA 1.29–3.09× on π0.5/X-VLA. Orthogonal to SnapFlow’s distill-everything (which we banked); relevant iff we ever run multi-step decode for quality at deployment — mean-of-10 batched draws is exactly that case (see #22’s re-ranked arm menu). Record-only; no arm here while the 1-NFE student holds the panel.

  • Second production data point for 1-NFE (2026-08-09, RDT2 page): 5-step flow → 1-step distillation with on-the-fly teacher targets holds at 7B / 10k-hours scale (“UltraFast”, fastest inference in their fleet at 2× π0.5’s size, +97 ms button-press reaction vs human). Same adopted-signal shape as our SnapFlow row.

  • Third axis on the family map: training-side integration supervision (2026-08-09, TCFM page, 2605.08511). Alongside distill it short (SnapFlow/OFP/MeanFlow) and smooth it in action time (FAFM), TCFM supervises multi-step integrated displacement during BC training (backprop through a 4-step Euler rollout) + a denoising-clock velocity-smoothness regularizer, then deploys RK4-30 (120 NFE). Their ablation is an interaction claim: smoothness loss alone or RK4 alone ~nothing; together 0%→70% long-horizon at 30–101-demo scale. Two reads for us: (a) the consistency term is corroborating evidence for the distill leg we already banked; (b) zero-training hook, priced not queued — score an RK4-k decode variant on a banked checkpoint vs the euler-10/30/Heun rows (decode flag only; SDN read says our fields are already smooth, so the integrator axis is separable). Prior is a null (our measured Heun gap is small); cheap falsification if the solver question resurfaces. RK4-120 is an eval-side anchor only — deployment stays 1-NFE.

  • 1-NFE mean read gains a ranking-fidelity number (2026-08-09, ForesightFlow page): their value baseline is exactly the 1-NFE endpoint estimate (x₀ + v(x₀,0,c), stop-grad), and they measure it: candidate ranking by 1-NFE preview agrees with NFE=100 at Kendall τ 0.80–0.86, top-1 ~87%, ~97% of the Monte-Carlo selection gain retained. A citable external anchor for how much the one-step mean read preserves ordering on our stack.