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# Source cleanup and validation
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The public 1.3B and 14B releases now expose the training path used by the
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reported three-encoder experiments: DINOv3, V-JEPA 2 and VideoMAEv2 features are
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combined by the hybrid Nyström–Monte Carlo MMD objective, and selected feature
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gradients are replayed to the generator.
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Earlier source snapshots also contained optional frame-level and plain
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Fréchet-distance objectives that were disabled in both released recipes. Their
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models, controllers, preprocessing tools, configuration fields and replay
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helpers have been removed so that the public tree follows the evaluated path.
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## Names that intentionally remain
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The code/video_fd directory remains part of both releases. Despite its
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historical directory name, it contains frozen encoder adapters, temporal
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sampling and decoder/encoder VJP utilities used by the active three-encoder MMD
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pipeline.
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Checkpoint methods and payload keys containing fd also remain where changing
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them would break loading existing three-encoder checkpoints. They describe the
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saved distribution-matching state; they do not enable a second loss branch.
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## Validation
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Run the root checks from the repository root in an environment with the release dependencies installed. They are self-contained and do not require an old Git commit:
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python3 scripts/check_readability.py
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python3 scripts/check_loss_parity.py --release 1.3B
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python3 scripts/check_loss_parity.py --release 14B
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python3 1.3B/scripts/test_cpu.py
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python3 14B/scripts/test_cpu.py
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check_readability.py verifies the cleaned file layout, rejects stale auxiliary
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loss references and configuration keys, parses every Python source, checks
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shell syntax, and confirms the main scientific settings of both public recipes.
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check_loss_parity.py independently composes the three encoder branches and
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checks the active MMD values, metadata, detached normalization and feature
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gradients on CPU.
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The first optimizer-step report in first-step-comparison.md records a historical
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comparison made before this cleanup. It remains useful evidence for the active
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training calculation, but it is not a source-identity claim for the cleaned
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tree.
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