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T

shaoxiongduan c6d0233eab [bugfix] WanTrack: track subsampling was a no-op when max_points == N

_augment_tracks gated the object-coverage + low-rank-weighted + uniform draw on
`0 < max_points < N`, so with the default WANTRACK_MAX_POINTS=2500 and the full
2500-track grid (max_points == N) the entire subsample block was skipped -- the
model saw all 2500 tracks every step and only chunk-masking ran. Gate on
`min_points < N` instead so K~U[1000,2500] actually samples (verified: kept
1179/1437/1573 across steps, was a constant 2500).

Also: TrackValidation now applies the same sampler (loads object_ids/track_weights,
runs _augment_tracks deterministically) so validation is conditioned on -- and
overlays -- the sampled subset, not the full grid. Env-gated WANTRACK_DEBUG=1 logs
per-step sampling stats (kept count, object coverage, coord ranges). Adds a
single-video (golf) overfit config for debugging.

2026-07-03 08:18:44 +00:00

ode_init_self_forcing_wan_causal

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

worldmodel

[bugfix] WanTrack: track subsampling was a no-op when max_points == N

2026-07-03 08:18:44 +00:00

README.md

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

README.md

Training Scenarios

End-to-end multi-step training pipelines. Each scenario directory contains all configs, scripts, and data needed to run a complete workflow.

scenario/
└── ode_init_self_forcing_wan_causal/   # KD → export → Self-Forcing

See the usage.md inside each scenario for step-by-step instructions.

For single-step configs, see examples/train/configs/.