Streaming video world models generate the future chunk by chunk, conditioned on their own past and a stream of actions. Trained on short clips, they drift once a rollout exceeds the training horizon, as prediction errors re-enter the context and compound. A common remedy is to train the model on its own rollouts via distribution matching distillation (DMD). For action-conditioned world models, the teacher becomes a bottleneck: an external video model is agnostic to the actions, whereas the model's own pretrained network drifts in the same manner as the student. We observe that a long rollout need not match a teacher, provided it remains a realistic continuation of its own scene that follows the commanded actions. Realism and scene fidelity can be measured against real-data statistics, which remain valid at any horizon, while action following is preserved by the model's conditional generation loss. We propose Fréchet Forcing, which fine-tunes a world model together with its deployed multi-step sampler under three constraints. Generative anchoring applies the flow-matching loss after a self-generated history ending on real data. Context fidelity, a Fréchet distance (FD) on latent statistics of the free rollout centred on each clip's own ground truth, keeps every rollout faithful to its scene. Reality matching, an FD to real frames in a frozen perceptual space, keeps the generated frames realistic. Because all three targets are derived from short real clips without comparing a rollout against a long recorded future, training requires no teacher, no online critic and no long videos. Fine-tuning MiniWorld-1B, pretrained on 253-frame clips, for at most 36 GPU-hours on 381-frame clips yields rollouts that remain stable beyond 4,000 frames and continue to follow their actions in both robot manipulation and camera-controlled exploration.
Figures & tables
Figure 1: 4,000-frame chunk-by-chunk rollouts from a single real frame on two DROID episodes (top four rows) and one Sekai scene (bottom two rows), following the commanded robot or camera trajectory. MiniWorld-1B, pretrained on 253-frame clips, cannot sustain longer rollouts: on DROID, saturated lattice texture spreads from the moving arm within 1,000 frames, and on Sekai the rollout darkens within the first 250 frames and then fades to black. MiniWorld-1B-FF, fine-tuned with Fréchet Forcing on 381-frame clips, stays coherent to frame 4,000, over 10× the fine-tuning horizon.
DMD
long-horizon DMD
RMD
self-history
reversal
Ours
Target
teacher
teacher score
per-chunk
GT
GT
real-data
score
on windows
teacher score
continuation
start
moments
Teacher-free
✗
✗
✗
✓
✓
✓
Critic-free
✗
✗
✗
✓
✓
✓
Gradient into the rollout
✓
✓
✓
✗
✗
✓
Original sampler kept
✗
✗
✗
✓
✓
✓
Table 1: Comparison of training signals for self-generated rollouts or histories. Categories include DMD (Self Forcing [ 15 ] ), long-horizon DMD (Self-Forcing++ [ 7 ] , Rolling Forcing [ 24 ] and LongLive [ 40 ] ), RMD [ 8 ] , self-history (Resampling Forcing [ 9 ] , SVI [ 20 ] and AR Forcing [ 41 ] ), and reversal (BAgger [ 29 ] and LIVE [ 14 ] ). GT denotes ground truth.
Figure 2: Long rollouts on 32 held-out DROID episodes (top) and Sekai clips (bottom), in the horizon bins of Table 2 . CLIP-FD, FVD and flicker are pooled over rollouts per bin, and dotted lines show GT at the same sample count for CLIP-FD where GT exists. Brightness is computed per rollout relative to the mean of its own GT in 60-frame windows, showing the median and interquartile range over rollouts. The shaded region marks the 381 frames of ground truth in each training clip of Fréchet Forcing, whereas MiniWorld-1B was pretrained on 253-frame clips. On Sekai the recorded camera trajectory ends at frame 1,789 and continues with the relative motion of other held-out clips.
DROID
Sekai
1–253
253–509
509–1k
1k–1.8k
1.8k–3k
3k–4k
1–253
253–509
509–1k
1k–1.8k
1.8k–3k
3k–4k
CLIP-FD ↓
GT
6
10
5
5
5
5
128
131
122
110
–
–
MiniWorld-1B
38
61
105
223
348
431
292
447
490
524
554
609
FM-only
38
66
117
217
320
401
238
371
419
476
505
564
MiniWorld-1B-FF
30
42
38
39
40
44
148
181
197
213
209
226
Table 2: Realism over the horizon on 32 held-out rollouts per domain, in bins of generated frames. MiniWorld-1B was pretrained on 253-frame clips and Fréchet Forcing fine-tunes on 381-frame clips. CLIP-FD is the Fréchet distance to real frames in CLIP space, reported next to that of GT with the same number of frames as the floor for each bin, and FVD uses I3D on 16-frame windows. Lower is better for both, while brightness is best at 1 and flicker at the GT’s value. On Sekai GT exists up to frame 1,789. In a paired bootstrap over rollouts (500 resamples), MiniWorld-1B-FF improves on the CLIP-FD of MiniWorld-1B in every bin of both domains in 100% of resamples, and on its FVD from frame 509 onwards on DROID and in every bin on Sekai in at least 97%.
DROID: PSNR to GT (dB)
Sekai
turn (frame 893)
turn (frame 3,581)
1–253
253–509
509–1k
1k–1.8k
1.8k–3k
3k–4k
yaw r
dir.
mag.
dir.
mag.
MiniWorld-1B
25.0
21.8
19.8
15.7
11.3
8.9
0.23
59
19
47
− 0.2
FM-only
24.1
21.4
19.6
16.3
12.9
10.4
− 0.13
47
2
38
− 2.8
MiniWorld-1B-FF
24.4
22.1
22.4
22.1
21.8
21.5
0.69
100
60
97
49
Table 3: Action following on the same held-out sets (higher is better). DROID: PSNR (dB) against GT at the same frame, in the bins of Table 2 . Sekai: correlation r between commanded and VGGT-estimated yaw in frames 1,021–1,789 of the recorded trajectories (recorded video: 0.92), and turn branches forked at frame 893 of the recorded trajectories and at frame 3,581 of the long rollouts: the percentage that turn in the commanded direction (dir.) and the percentage of the commanded 70.5∘ they reach (mag.). In the bootstrap of Table 2 , the DROID PSNR of MiniWorld-1B-FF exceeds that of MiniWorld-1B from frame 253 onwards in 100% of resamples.
Figure 3: Training dynamics of distribution matching on free rollouts. (a) Context dependence in frames 765–933, measured by the regression slope of each rollout’s mean latent feature on that of its own ground truth across held-out clips (1 indicates full preservation of clip-specific appearance). (b) Cosine similarity between the mean-matching correction and the true direction needed by the gradient-carrying samples, showing per-step training logs (dots) and a 50-step moving average (lines). (c) Held-out CLIP-FD in frames 1,021–1,789 for FM-only, the graded latent FD alone, and the graded latent FD combined with the perceptual FD. The dotted curve tracks the training latent FD of the graded variant, which continues to decrease even as held-out CLIP-FD rises.
1–125
125–253
253–509
509–1k
1k–1.8k
GT
1.03
0.99
1.01
1.00
1.02
MiniWorld-1B
1.16
1.28
1.38
1.44
1.45
Re-rendered to the end, ta=0.7
1.31
1.28
1.24
1.32
1.35
Re-rendered to the end, ta=0.4
1.20
1.20
1.15
1.24
1.29
Ends on a real chunk
1.07
1.03
1.03
1.08
1.13
at 225 steps (MiniWorld-1B-FF)
1.09
1.05
1.05
1.07
1.13
Table 4: Chunk-boundary flicker over the horizon: block-scale change across a chunk boundary relative to the change within a latent (1: no redraw at the chunk rate), in bins of generated frames, on 32 held-out clips. The fine-tuned rows differ only in how the history of generative anchoring ends, and all are evaluated at 150 steps except the last.
CLIP-FD
FVD
sharp.
bright.
flicker
yaw r
turn
MiniWorld-1B-FF
226
518
1.33
1.03
1.13
0.69
1.00
context fidelity in pixels
230
336
0.70
0.79
1.03
0.64
0.97
history re-rendered to end
248
463
1.42
0.94
1.35
0.57
0.94
− context fidelity
264
540
0.68
0.74
1.29
0.59
0.91
− self history
253
564
0.77
0.44
1.13
0.63
0.91
− reality matching
395
745
0.58
0.84
1.39
0.23
0.75
Table 5: Ablations on the 1,789-frame Sekai protocol, frames 1,021–1,789 of the recorded trajectories. Sharpness and brightness are relative to GT (1 is ideal), flicker is the chunk-boundary measure (GT: 1.02), and turn is the fraction of branches forked at frame 893 that turn in the commanded direction. Rows 4–7 use the re-rendered history of row 3 and are compared with it, with rows 4–5 cumulative.
Autoregressive video diffusion models have emerged as a foundation for long-horizon world rollout. However, when extended far beyond their training windows, these models suffer from progressive drift: visual quality deteriorates, motion becomes inconsistent, and object identity gradually collapses as generated frames are recursively reused as context. We study a supervision-horizon gap: short-horizon distillation leaves later rollout states without direct supervision. We show that matching short-window marginals need not determine long-horizon behavior, while longer on-policy trajectories expose delayed failures to training. This motivates temporal manifold matching at scale for long-horizon world models. Building on this insight, we introduce LongForcing, a distillation approach that scales teacher supervision to long student self-rollouts. LongForcing preserves the short-horizon teacher-forcing and causal ODE-distillation stages, while replacing the realscore teacher in the final distribution-matching stage with an extended-horizon bidirectional teacher. The long-horizon teacher supervises extended student self-rollouts using the standard distribution-matching formulation, without modifying the student architecture or increasing inference cost. We evaluate LongForcing on text-to-video generation and interactive action-conditioned world models, achieving a VBench Quality score of 85.52 and improving WorldRoamBench 60s action score by 23.9 points in first-person ablations. Qualitative stress tests further sample interactive rollouts up to 24 hours, far beyond the training horizon. These results suggest that scaling supervision along the temporal axis is a practical step toward long-horizon world models. The project website and code are at https://github.com/Fantasy-AMAP/LongForcing.
Video world models predict future observations conditioned on historical observations and control signals, enabling long-horizon generation through autoregressive state transitions. Unlike conventional video generation models that primarily capture visual appearance and motion, video world models learn the underlying dynamics governing environment evolution under agent actions, providing a foundation for embodied AI and interactive simulation. Recent progress has largely relied on adapting pretrained video generation models through post-training or distillation. Although effective, these approaches often require complex training pipelines, substantial computational resources, and suffer from the mismatch between bidirectional pretraining and causal streaming inference. Recent studies have shown that training autoregressive video world models from scratch is feasible and scalable. However, the community still lacks a lightweight, transparent, and fully reproducible baseline trainable end-to-end with modest computational resources. We present MiniWorld, a reproducible framework for training streaming video world models from scratch. MiniWorld employs a block-causal Video Diffusion Transformer trained with Flow Matching in the latent space of a pretrained Video VAE. Building on Diffusion Forcing, it adopts a chunk-wise non-decreasing noise schedule and two-stage continued training to improve temporal modeling and stability. During inference, MiniWorld combines a rolling KV cache with pipelined asynchronous denoising for efficient streaming generation under bounded computation. The entire model can be trained within several days on a single 8-GPU server. By releasing the training and inference codebase and pretrained checkpoints, we hope MiniWorld will facilitate future research on video world modeling.
Video world models enable interactive exploration of dynamic environments, yet struggle to respect prior observations over long horizons and across viewpoints. We present WorldCrafter, a video world model that learns a camera-queryable implicit 3D-aware memory for this purpose. The key insight is to let the requested viewpoint shape how multi-view evidence is compressed into the video generator's limited token budget. Trained jointly with the video generator, a memory encoder and pose-conditioned readout module integrate historical observations into a fixed set of target view-specific tokens before denoising, without explicit depth-based correspondences. By combining this memory with recent temporal context and few-step distillation, WorldCrafter enables streaming scene exploration from a single input image or text prompt. Experiments across static and dynamic scenes show substantial gains in long-horizon consistency and camera-control accuracy while preserving visual quality during minute-scale exploration.