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Parametric Trajectory Distillation for Few-Step Video Generation

Lan Feng, Peter Karkus, Maximilian Igl, Julius Berner, Yuxiao Chen, Shuhan Tan, Alexandre Alahi, Boris Ivanovic, Marco Pavone

arXiv:2610.11498v1cs.CV

TL;DR

Few-step distillation must match a teacher’s iterative generation with much less sequential computation, while trajectory methods can ask students to reproduce transitions too curved for their capacity. PTD fits polynomial segments using teacher guidance along the student’s predicted path, retaining the curvature head only during training. It improves over strong four-step baselines on Wan2.1-14B and MiniMax-H3, with human preference shares of 55.1% against PDD and 63.4% against LightX2V Turbo.

  • Problem

    Few-step students have far less sequential computation to reproduce teacher generation, and trajectory supervision can blur detail when students cannot represent complex transitions.

    Source: §1 · p. 2

  • Method

    PTD predicts polynomial trajectory segments and learns from teacher velocities queried along its own predicted path, using one loss for displacement and shape.

    Source: §1 · p. 2

  • Results

    PTD improves over PDD on Wan2.1-14B and over LightX2V Turbo on MiniMax-H3; human preference shares are 55.1% against PDD and 63.4% against LightX2V Turbo.

    Source: §1 · p. 2, §5 · p. 9

  • Takeaways & Limitations

    PTD retains trajectory supervision while adapting the learned curve to the student’s predictive capacity, and removes the curvature head at inference.

    Source: §1 · p. 2

Abstract

from arXiv · show

Video diffusion and flow models require many sequential evaluations, making generation computationally expensive. Few-step distillation reduces this cost but poses a capacity allocation problem: a student must match the teacher's iterative generation with far less sequential computation. Existing trajectory methods ask the student to reproduce teacher transitions that are highly curved at high noise, which can exceed its capacity and degrade fine detail. We introduce Parametric Trajectory Distillation (PTD), which lets the student parameterize teacher trajectory segments as polynomials and learn from teacher guidance along its own predicted path. PTD is designed to let the learned curvature adapt to the backbone's predictive capacity, preserving motion and diversity. The curvature head is used only in training; inference keeps the original backbone architecture. On Wan2.1-14B, four-step PTD sets a new state of the art for trajectory distillation, significantly improving dynamic quality and naturalness over PDD, the best-performing trajectory-only method on this model, under the same training setting. On the 33B audio-video MiniMax-H3, LoRA-trained PTD significantly improves diversity and naturalness over the state-of-the-art LightX2V Turbo. Blinded human votes give PTD 55.1% and 63.4% preference shares against PDD and LightX2V Turbo. Project page: https://alan-lanfeng.github.io/PTD/.

1 INTRODUCTION

Few-step distillation asks a student with far less sequential computation to reproduce a teacher’s generation process. PTD fits teacher-guided polynomial segments along the student’s predicted path, and improves results over strong four-step baselines on Wan2.1-14B and MiniMax-H3.

Motivation

  • Few-step distillation gives a student with potentially the teacher’s parameter count far less sequential computation to reproduce the teacher’s generation process.The paper frames this as a capacity-allocation problem: supervision must help approximate a longer generation process with comparable sample quality.Source: §1 · p. 2
  • Trajectory supervision can blur fine detail when a low-capacity student averages complex transitions, while distribution matching can reduce sample diversity.PTD is proposed as a way to combine trajectory correspondence with guidance on the student’s own path, without stacking objectives.Source: §1 · p. 2

Approach

  • PTD predicts a polynomial approximation of a teacher trajectory segment and queries teacher velocities along that student-predicted path.A single velocity-regression loss trains both outputs, while inference retains the mean-velocity endpoint update and removes the curvature head.Source: §1 · p. 2

Results

  • PTD sets a new state of the art for trajectory distillation on Wan2.1-14B and improves over PDD; on MiniMax-H3 it improves diversity and naturalness over LightX2V Turbo.Blinded human votes give PTD preference shares of 55.1% against PDD and 63.4% against LightX2V Turbo.Source: §1 · p. 2

2 RELATED WORK

Related work spans methods that learn endpoint or flow-map consistency, explicit trajectories, and distribution matching. PTD is distinguished by querying teacher velocities on its own predicted curve while using trajectory supervision alone.

Endpoint and flow-map learning

  • Consistency models learn flow maps whose semigroup property underlies progressive distillation and shortcut models; MeanFlow learns interval-average velocity.PTD’s h is an interval-average velocity fitted to teacher velocities rather than self-consistency targets.Source: §2 · pp. 2–3

Explicit trajectories and local supervision

  • PTD queries teacher velocities along its predicted curve and uses analytic basis derivatives, then drops the curvature head at inference.This differs from PDD’s regression to teacher Runge–Kutta targets and π-Flow’s integration of a compact policy at inference.Source: §2 · p. 3

Distribution matching for video

  • PTD uses trajectory supervision alone and significantly improves Astra diversity and naturalness over a state-of-the-art DMD student.The passage contrasts this with DMD and related video methods that use score-based supervision or distribution-level refinement.Source: §2 · p. 3

3 PARAMETRIC TRAJECTORY DISTILLATION

PTD represents each denoising hop with a mean velocity and a polynomial curvature path, then fits the predicted path to teacher velocities. Student-curve queries provide local guidance without dense teacher integration, and inference uses only the mean-velocity endpoint update.

3.1 ONE PREDICTION FOR A TRAJECTORY SEGMENT

  • Each student evaluation predicts a mean-velocity vector and curvature coefficients that define a polynomial denoising segment over one fixed-grid hop.The basis functions vanish at both endpoints, so curvature changes the interior shape while the mean velocity determines the endpoint displacement.Source: §3 · p. 3, §3.1 · pp. 3–4

3.2 A SINGLE LOSS ON THE STUDENT CURVE

  • A single least-squares objective fits student-predicted velocities to detached teacher velocities queried at positions on the current student curve.Residuals jointly supervise the mean velocity and curve coefficients; the expected mean-velocity gradient targets the teacher velocity averaged along the student curve.Source: §3.2 · p. 4
  • On strongly curved, high-noise hops, dense teacher integration costs many sequential evaluations and its endpoint target is difficult for a limited-capacity student to learn.On H3 teacher trajectories, two curve terms capture 46% of within-hop velocity variation near the noise end versus 90% on the final hop.Source: §3.2 · p. 4
  • Training queries teacher velocities along the student’s current curve rather than marching the teacher densely to construct endpoint targets.The queries coevolve with predicted coefficients; for H3, the final hop is instead marched, improving detail at about 20% more training compute, while Wan uses student-curve queries on every hop.Source: Figure 3 · p. 5, §3.2 · p. 5
  • Two teacher queries per hop are the default, independent of the number of curvature coefficients, and each query jointly supervises the velocity profile.The default samples one point uniformly from each half-interval; additional queries increase teacher cost without changing inference.Source: §3.2 · p. 5

3.3 TRAINING AND INFERENCE

  • At inference, each hop updates the latent using the predicted mean velocity, while the curvature head and interior-curve evaluations are discarded.Training proceeds on states produced by successive student hops, carrying each detached endpoint forward before restarting the rollout from noise.Source: §3.3 · p. 5

4 EXPERIMENTS

The experiments compare four-step PTD with LightX2V Turbo on MiniMax-H3 and PDD on Wan2.1-14B, then test how supervision and curve capacity affect learning. PTD improves key quality measures against both baselines, while ablations examine detail retention, target accuracy, and learnable curvature.

4.1 BASELINES AND EVALUATION PROTOCOL

  • The evaluation uses blinded human votes and GPT-6 Astra judgments as primary evidence, with VBench reported as a reference.The Wan comparison reproduces PDD with the same backbone, frozen teacher and guidance, training prompts, batch size, and learning rate as PTD.Source: §4.1 · p. 6, §4.3 · p. 6

4.2 MINIMAX-H3: COMPARISON WITH LIGHTX2V TURBO

  • PTD raises Astra diversity from 1.55 to 2.81 and naturalness from 2.98 to 3.09 over LightX2V Turbo, matches dynamic and static quality, and receives 63.4% blinded preference.The comparison uses a 33B joint audio-video model trained with LoRA; inference uses four network evaluations.Source: §4.2 · p. 6

4.3 WAN2.1-14B: COMPARISON WITH PDD

  • Against PDD, PTD raises Astra dynamic quality from 2.16 to 2.43 and naturalness from 2.60 to 2.82, matches static quality, and receives 55.1% human preference.PTD at 64 updates also exceeds the overall VBench score of PDD checkpoints at 64, 96, and 256 updates; Table 1 compares overall, quality, and semantic VBench scores against PDD at 96 updates.Source: §4.3 · pp. 6–7, Table 1 · p. 7, Figure 4 · p. 7
  • On Wan2.1-14B’s high-noise first hop, PTD’s bend magnitude ratio is 0.081 versus PDD’s 0.024 at 64 updates, while final-hop projections remain close.PTD also has a higher teacher-aligned projection on the first hop at every shared update count from 32 to 224.Source: §4.3 · pp. 7–8, Figure 5 · p. 8

4.4 ABLATIONS

  • Teacher marching on every hop softens local detail, while both student-curve variants retain distinct detail in the dog’s coat and the painter’s hand and brush.The comparison uses Wan2.1-1.3B students trained for 40 updates, all with four-step inference.Source: §4.4 · p. 8, Figure 6 · p. 8
  • On hops 0–2, two-query quadrature MSE is 12.6%, 13.6%, and 13.3% of student prediction-error energy; three queries reduce these ratios to 6.6%, 6.1%, and 5.8%.The third query costs 50% more teacher queries per hop.Source: §4.4 · p. 9, Figure 7 · p. 9
  • Increasing K from 2 to 8 raises offline explained variation from 46% to 90% on the high-noise hop and from 90% to 99% on the final hop, but student-feature probes explain only 7–11% on the first hop.The learned curve is a shrunken bend aligned with the teacher’s; the study adopts K=8 as its default.Source: §4.4 · p. 9

5 CONCLUSION

PTD uses student-predicted polynomial paths to guide trajectory distillation, with curvature shaping training but not inference. The reported results improve on prior methods across MiniMax-H3 and Wan2.1-14B, while analyses find that supervision should vary along the trajectory.

5 CONCLUSION

  • PTD sets a new state of the art for trajectory distillation on Wan2.1-14B and improves Astra dynamic quality and naturalness over PDD under the same training setting.On MiniMax-H3, PTD improves Astra diversity and naturalness over LightX2V Turbo while matching its dynamic and static quality; blinded votes prefer PTD 63.4% against Turbo and 55.1% against PDD.Source: §5 · p. 9
  • Teacher queries along student-predicted polynomial paths supervise mean velocity and curvature during training, but inference retains four-step sampling and the original backbone architecture.The curvature head is removed at inference.Source: §5 · p. 9
  • The best supervision target varies along the trajectory: student-curve queries retain fine detail, while a teacher-marched target improves detail on the smoother final hop.Although the curve family can fit teacher trajectories almost exactly, the learned curve is shrunken and teacher-aligned so its targets remain learnable.Source: §5 · p. 9
  • The released reproducibility materials are described alongside the hop grid, training recipes, evaluation protocols, and judging rubric.The listed materials include trained checkpoints or adapters, code, evaluation prompts, and judging scripts.Source: REPRODUCIBILITY STATEMENT · p. 10

A ADDITIONAL METHOD DETAILS

The appendix details endpoint-constrained polynomial curves, finite-query gradient routing, and the conditions under which local velocity fitting controls a distilled endpoint.

A.1 ENDPOINT-CONSTRAINED CURVES AND SUPERVISION

  • The curvature basis vanishes at both endpoints, so curvature changes the path interior without changing its endpoints; the endpoint output and curve coefficients receive supervision from the same local velocity residual.The basis functions are built from degree-j Legendre polynomials, and the curvature coefficients are latent-sized vectors emitted by one head.Source: §A.1 · p. 15
  • With K=8, random query positions make the polynomial basis fully observable across training draws, although a single pair of queries supplies at most two independent constraints per latent coordinate.The equal-weight stratified pair estimates the uniform integral of the squared residual.Source: §A.1 · p. 15

A.2 FINITE-QUERY GRADIENT ROUTING

  • The routed objective preserves the loss value and endpoint-output gradient before modality normalization, while changing the curvature gradient by omitting a term whose expectation is a covariance.The shared backbone still receives gradients through both outputs.Source: §A.2 · p. 16

A.3 FROM LOCAL VELOCITY FITTING TO A DISTILLED ENDPOINT

  • Under a Lipschitz teacher field, the expected stratified-query objective equals the integrated squared residual and bounds endpoint error; a vanishing residual throughout the hop recovers the teacher path.In that zero-residual case, the endpoint output is the teacher trajectory’s average velocity, while curvature represents the path during training.Source: §A.3 · p. 16

A.4 RELATION TO LAGRANGIAN FLOW MAPS, TVM, PDD, AND π-FLOW

PTD is framed as a restricted Lagrangian flow-map approach: one student evaluation parameterizes a polynomial path over an interval, and stratified teacher queries supervise it. The appendix contrasts its supervision, training assumptions, and deployment with related distillation methods.

PTD as a restricted Lagrangian map

  • Unlike LMD, which conditions a map on both times and uses a JVP for its time derivative, PTD uses a fixed polynomial basis and teacher velocities along its student-predicted curve.The cited LMD formulation reports a JVP cost of about 1.5 times a forward pass.Source: §A.4 · pp. 17–18
  • PTD evaluates its interval coefficients once at the teacher’s single-time interface, and its equal-weight stratified loss is an unbiased estimate of the interval objective in value and semigradient.Its grid-fixed start times and polynomial target-time basis permit one student pass for multiple target times without a JVP.Source: §A.4 · pp. 17–18
  • Under a Lipschitz teacher field, PTD’s interval residual bounds the discrepancy between the student map and the teacher’s one-interval pushforward of the same start-state distribution.This is presented as an interval-level analogue of the Lagrangian Wasserstein bound.Source: §A.4 · p. 18

Terminal Velocity Matching

  • TVM trains from scratch with self-generated terminal-velocity targets and a data flow-matching loss; PTD uses a frozen teacher on student-curve states within each interval.The comparison describes TVM’s model velocity as Lipschitz and PTD’s bound as using the frozen teacher’s Lipschitz constant.Source: §A.4 · p. 18

PDD

  • PDD regresses selected interval-velocity slots to teacher midpoint Runge–Kutta targets, whereas PTD queries exact teacher velocities on its current curve and uses the residual to bound endpoint error.The PDD midpoint target differs from the exact mean velocity by solver truncation error; H3’s final interval instead uses an eight-step Euler teacher march.Source: §A.4 · p. 19

π-Flow

  • For PTD’s time-only policy, on-policy imitation and its interval objective coincide, but PTD deploys only the interval-average output rather than integrating a policy with inference substeps.The cited π-Flow methods for state-dependent policies have the same loss value but different gradients from the semigradient objective.Source: §A.4 · p. 19

B IMPLEMENTATION DETAILS

The implementation uses a shifted four-hop time grid, with modality-specific audio timing for MiniMax-H3 and a shared 128-slot grid for the Wan2.1-14B students.

B IMPLEMENTATION DETAILS

  • The shifted grid gives four hops at t = (0.999, 0.947, 0.857, 0.667, 0), with interval widths ∆ = (−0.052, −0.090, −0.190, −0.667).The shift is γ=6; the first time value is clamped.Source: §B.1 · p. 19
  • MiniMax-H3 uses the release audio coupling τd = 0.25t/(1 − 0.75t), while LightX2V Turbo retains its released video shift-6 and audio shift-3 sampler settings.Source: §B.1 · p. 19
  • Both Wan2.1-14B students use four blocks of 32 slots from the same 128-slot shift-6 grid.Source: §B.1 · p. 19

B.2 TRAINING CONFIGURATIONS

The evaluated PTD checkpoints use different backbone-specific training recipes, and the H3 recipe adds teacher marching on the final hop. Training also uses prompt-only rollouts, with no paired target videos.

B.2 TRAINING CONFIGURATIONS

  • Training prompts come from ViMix, OpenVid, and VidProM for H3 and randomly sampled ViMix prompts for both Wan students, without paired target videos.The H3 corpus contains 55,879 rewritten prompts; its source evaluation captions are excluded by a leakage filter.Source: §B.2 · p. 20
  • Wan and its PDD reproduction share a frozen teacher, CFG 5, SLG on layer 12, batch size 256, and learning rate 10−5; the reproduction is evaluated at 64, 96, and 256 updates.The PDD reproduction’s lower VBench scores than published values are attributed to prompt-data and evaluation-prompt differences, plus evaluation noise.Source: §B.2 · pp. 20–21
  • A complete rollout spans four reported iterations, with each iteration corresponding to one optimizer update.Workers carry detached student endpoints between hops and restart from noise after the fourth hop.Source: §B.2 · p. 21

B.3 H3 FINAL-HOP SUPERVISION

  • H3 final-hop supervision trains only the mean-velocity prediction from a teacher-marched endpoint, with no student-curve velocity loss or curvature-head supervision on that hop.The final hop is smoother than the hop nearest noise: offline fits with K=2 terms explain 90% versus 46% of velocity variation, respectively.Source: §B.3 · p. 21

B.2 TRAINING CONFIGURATIONS

  • H3 uses per-hop rank-128 LoRA and student-curve supervision on hops 0–2, then teacher-marches the final hop; Wan uses full fine-tuning and student-curve supervision on all four hops.The Wan run uses two stratified queries per hop; H3 uses two on hops 0–2 and an eight-step teacher-marched endpoint on the final hop.Source: §B.2 · p. 20, Table 3 · p. 20, §B.3 · p. 21

B.3 H3 FINAL-HOP SUPERVISION

  • H3 normalizes each modality’s loss by its detached value before combining modalities with audio weight 1.Wan has one modality and does not use this normalization.Source: §B.2 · p. 20, §B.3 · p. 21

B.4 REMOVING THE TRAINING SCAFFOLD

PTD removes its curvature head at inference because the head shapes training supervision rather than the deployed endpoint. Both backbones then sample in four steps without curvature evaluation.

B.4 REMOVING THE TRAINING SCAFFOLD

  • The curvature head is removed at inference because the endpoint does not require it.For H3, the adapted hop projection can be merged into the original projection while preserving layer types and output dimensions.Source: §B.4 · p. 21
  • H3 adapter merging materializes each hop’s projection as W + B(k)A(k), preserving the original projection shape and output dimensions.The shared frozen-base representation with active hop adapters computes the same linear maps in exact arithmetic, though storage differs.Source: §B.4 · p. 21
  • Both deployed samplers take four steps using only the mean-velocity prediction, retaining the original backbone topology and latent output interface.Wan retains its fully fine-tuned backbone and dense mean-velocity projection; it has no adapters to merge.Source: §B.4 · p. 21

C EVALUATION PROTOCOL AND ADDITIONAL RESULTS

PTD improves Wan’s VBench results over PDD and shows gains on specified Astra dimensions across both prompt groups; blinded raters prefer PTD in both model comparisons. The section also reports evaluation protocols and the cost-accuracy trade-off for two-query supervision.

C EVALUATION PROTOCOL AND ADDITIONAL RESULTS

  • Evaluation uses 100 prompts, three seeds per model, and four network evaluations; Astra judges sampled frames, while human raters compare full-video playback.The prompts comprise held-out live-action captions and generated action prompts; model identities are concealed in the judge mosaics and human comparisons.Source: C EVALUATION PROTOCOL AND ADDITIONAL… · pp. 21–22
  • Audio is not evaluated, and VBench is reported as a reference while Astra ratings and blinded human votes are the primary evidence.VBench uses 946 prompts and five seeds, whereas the published comparison rows use different prompt protocols.Source: C EVALUATION PROTOCOL AND ADDITIONAL… · pp. 21, 23
  • In both V50 and D50, H3 gains are +1.24 and +1.28 for diversity and +0.19 and +0.04 for naturalness; Wan gains are +0.29 and +0.24 for dynamic quality and +0.19 and +0.25 for naturalness.The paired 95% intervals exclude zero for each listed gain.Source: C EVALUATION PROTOCOL AND ADDITIONAL… · p. 22, Table 5 · p. 23
  • 1.28 overall VBench points separate PTD from PDD at matched 64 updates (84.57 vs. 83.29), and PTD scores higher on 13 of 16 dimensions than PDD’s best checkpoint.PTD’s 64-update checkpoint also exceeds all three evaluated PDD checkpoints in overall and quality scores.Source: C EVALUATION PROTOCOL AND ADDITIONAL… · p. 22, Table 6 · p. 24, Table 7 · p. 24
  • PTD receives 63.4% preference share against LightX2V Turbo on H3 and 55.1% against PDD on Wan in blinded pairwise voting.The final analysis contains 161 votes; ties are split equally in the preference-share calculation.Source: C EVALUATION PROTOCOL AND ADDITIONAL… · p. 25, Table 8 · p. 25

D.1 ACCURACY OF TWO-QUERY TARGET ESTIMATION

  • The query diagnostic compares estimates from sampled teacher velocities with reference mean velocity from endpoint displacement over signed hop duration; training queries the student-predicted path instead.It evaluates hops 0–2, while H3’s final hop uses a teacher-marched target.Source: §D.1 · p. 25
  • Two-query discrepancies equal 12.6%, 13.6%, and 13.3% of H3 student error across hops 0–2; three queries reduce these ratios to 6.6%, 6.1%, and 5.8%.Three queries cost one additional teacher evaluation per supervised hop, so the recipe retains two.Source: §D.1 · p. 25

D.2 WITHIN-HOP TRAJECTORY LEARNING ON WAN2.1-14B

On the strongly curved first hop, PTD’s learned bend is larger and more teacher-aligned than PDD’s at shared checkpoints. Their teacher-aligned projections are much closer on the smoother final hop.

D.2 WITHIN-HOP TRAJECTORY LEARNING ON WAN2.1-14B

  • The probe measures student and teacher bend vectors in the middle half of each hop, using magnitude ratio, directional alignment, and teacher-aligned projection.For one vector pair, q = r cos θ; the calibration ratio r/cos θ has a reciprocal equal to the least-squares bend-magnitude calibration when the inner product is positive.Source: §D.2 · p. 26
  • On the high-noise first hop, PTD exceeds PDD at every shared checkpoint from 32 to 224 updates in bend magnitude ratio, direction cosine, and teacher-aligned projection.At 64 updates, the respective values are 0.081 vs. 0.024, 0.171 vs. 0.139, and 0.014 vs. 0.003; the teacher direction turns 43.5° in hop 0 versus 4.7° in the final hop.Source: §D.2 · p. 26
  • At the best evaluated checkpoints, PTD also exceeds PDD on all three first-hop bend metrics: 0.081 vs. 0.039 for r, 0.171 vs. 0.155 for cos θ, and 0.014 vs. 0.006 for q.Those checkpoints are PTD at 64 updates and PDD at 96 updates.Source: §D.2 · p. 26
  • The teacher-aligned projections of PTD and PDD differ by at most 0.005 on the smoother final hop at every shared checkpoint.The H3 recipe applies teacher marching on this final hop.Source: §D.2 · p. 26

D.3 CURVE EXPRESSIVENESS AND FROZEN-FEATURE PREDICTION

The H3 diagnostics contrast offline trajectory fitting with frozen-feature velocity prediction; final-hop probes are strongest and motivate teacher marching only there.

D.3 CURVE EXPRESSIVENESS AND FROZEN-FEATURE PREDICTION

  • Offline fitting centers teacher velocities, fits their remaining variation with weighted derivative fields, and measures explained velocity-deviation energy across trajectories.The basis comparison uses 1,000 trajectories, with 20 fitting and 20 held-out trajectories for offline family comparisons; frozen-feature probes use a separate fitting and holdout split.Source: §D.3 · pp. 26, 28
  • At 10^-3, final-hop probes score near 45% for ridge and 49–50% for Adam; K=2 explains 90% of final-hop variation versus 46% near the noise end.Earlier-hop held-out scores are 7–11%, 14–15%, and approximately 3.5% for hops 0, 1, and 2, respectively; the findings motivate teacher marching only on the final hop.Source: §D.3 · p. 28, Table 12 · p. 28
  • The population Gram matrix summarizes conditioning across query positions, while the coefficient sweep uses unit-RMS fields and reports Adam ranges after 40 batches.Natural-scale and unit-RMS coordinates are distinguished in Table 12.Source: §D.3 · pp. 28–29, Table 13 · p. 29

E EXTENDED RELATED WORK

  • PTD’s h is the interval-average velocity learned by MeanFlow, but its targets come from the frozen teacher rather than student self-consistency or Jacobian-vector products.The related-work discussion also places consistency and progressive-distillation methods within learning finite-interval flow maps.Source: E EXTENDED RELATED WORK · p. 29
  • PTD predicts sample-dependent coefficients for an endpoint-preserving polynomial curve and deploys its displacement; related work spans trajectory parameterizations, distribution-level supervision, H3 accelerators, and few-step evaluation.For Wan2.1-14B, published four-step students score above the 50-step teacher in overall VBench, so the paper uses blinded votes and GPT-6 Astra judgments as primary evidence.Source: E EXTENDED RELATED WORK · pp. 29–30

F VISUAL PROMPT FOR THE FIRST-PAGE EXAMPLES

  • Figure 1’s snowboarding example compares the 50-step MiniMax-H3 teacher with four-step LightX2V Turbo and PTD at five matching timestamps using the same center crop.The crop retains 80% of the original width and height.Source: F VISUAL PROMPT FOR THE FIRST-PAGE… · p. 31

F.1 SNOWBOARDING

  • The snowboard prompt describes a fast, low tracking shot that follows a rider carving downhill, then cuts to a wide shot of a hard turn and powder spray.The second shot begins at 00:02.800.Source: §F.1 · p. 31

G GPT-6 ASTRA EVALUATION RUBRIC

  • Automated evaluation uses GPT-6 Astra at medium reasoning effort to score each row’s three clips on four integer 1–4 rubric metrics; the paper reports these scores and prompt-level outcomes derived from their differences.Each call receives 12 chronologically ordered 2 × 3 mosaic frames, with one model per row and seeds 0/1/2 across columns. For each prompt and model pair, each row ordering is judged twice, giving four calls in total.Source: G GPT-6 ASTRA EVALUATION RUBRIC · p. 32
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