[RyanPCo]

scheduler: harden warmup_stable_decay (min_factor floor, argument checks)

Decay now ends at an optional min_factor instead of 0, arguments are
validated, and a zero-length warmup or decay no longer divides by zero.
Signature stays compatible with existing callers.

Co-Authored-By: Claude Opus 5.5 noreply@anthropic.com
Claude-Session: https://claude.ai/code/session_01X86xQuci4asKUkiCwqMiKF

configs: WSD recipes define their scheduler in-config via _scheduler_reset

A Hydra merge onto the model's LinearLR keeps its arguments, so the launch
scripts swapped WSD in from the CLI. _scheduler_reset (ported from #710) nulls
model.scheduler in the defaults chain so op2's self can define WSD, and every
recipe built on it sets the phases the launchers computed (1k or 500 warmup,
linear decay over the last 20% of max_epochs x 100 steps; overfit1 6k steps).
The full* configs had no launcher and now follow the same rule. The generators
emit the same blocks. The launchers' '~model.scheduler' swap still works.

Co-Authored-By: Claude Opus 5.5 noreply@anthropic.com
Claude-Session: https://claude.ai/code/session_01X86xQuci4asKUkiCwqMiKF

trainHydra: tag W&B runs with their hydra_configs/experiments/

Ported from #710: a config composed as experiments// gets
appended to logger.wandb.tags. Skipped when Hydra is not initialized
(train() called directly from tests).

Co-Authored-By: Claude Opus 5.5 noreply@anthropic.com
Claude-Session: https://claude.ai/code/session_01X86xQuci4asKUkiCwqMiKF

trainHydra: mode=validate scores one checkpoint through the training val heads

Ported from #710 without its metric_sampling, named validation sets or
open-loop pieces: builds the same val heads as training (train_viz,
unseen_op_valid, extra_valid), runs trainer.validate on one device with no
logger, and writes /validation_metrics.json.

Fixes the port's EMA gap: a checkpoint with checkpoint["ema"] is scored with
the EMA weights, as training validated it, even under a config without
model.ema enabled. limit_val_batches is scaled by the training world size so
one rank covers the frames the metric loaders were built for, and a validate
job never swaps its ckpt_path for a requeue's last.ckpt.

Co-Authored-By: Claude Opus 5.5 noreply@anthropic.com
Claude-Session: https://claude.ai/code/session_01X86xQuci4asKUkiCwqMiKF

Ported from ideas in #710 (no IDs taken). Combined stack: ruff clean; 562 targeted unit tests pass on CPU; no GPU smoke run yet.

🤖 Generated with Claude Code

https://claude.ai/code/session_01X86xQuci4asKUkiCwqMiKF

[RyanPCo]

Warning

This pull request is not mergeable via GitHub because a downstack PR is open. Once all requirements are satisfied, merge this PR as a stack on Graphite.
Learn more

• history: learned null token for dropped and episode-start-padded history #715
• mecka private: fixed-budget operator-scaling ladder generator #714
• train: hardened WSD defined in-config, W&B experiment tag, mode=validate on EMA weights #713 👈 (View in Graphite)
• val: exact-index metric frames, pad-masked metrics with thirds, per-loader eval RNG #712
• mecka private: mono converter, opscale configs and generators, IDs kept out of the repo #709
• data/rdt: keypoint RDT recipe, pose-glitch masking, hardened staging #708
• pl_model: RDT-style weight EMA, validated in place of raw weights #701
• data: stage episodes to node-local disk; never read them from /workspace #707
• pi: keep per-sample intrinsics so overlays use the episode camera #690 : 2 other dependent PRs (#691 , #716 )
• rdt: long-range visual memory; image memory window and history stride in seconds #689
• norm stats: cache sampled rows per episode so splits share them #687 : 1 other dependent PR (#688 )
• data: exclude episodes by hash; scan Mecka egomotion for failed head tracking #686
• eval: log validation videos to wandb #684 : 1 other dependent PR (#685 )
• slurm: stop cancelled jobs from leaking whole GPUs #682 : 1 other dependent PR (#683 )
• algo: RDT (Robotics Diffusion Transformer) on the HPT chassis #680 : 1 other dependent PR (#681 )
• pi: initialize from PaliGemma with a scratch action expert #677 : 1 other dependent PR (#678 )
• perf: vectorized image augs and a torch.compile knob for HPT and Pi; Pi clips gradients like openpi #676
• loader: one zarr read per key, not one per keymap entry #674
• eval: build the pinned-episode video batches only where they are used #673
• checkpoints: last.ckpt as a symlink, not a second full copy #672
• trainHydra: metric_frames_per_episode: auto, and warn on empty prompts #671
• transforms: per-step wrist framing for proprio history #670
• viz: one keypoint renderer, the horizon trace #669
• configs: history and backbone-LR knobs on the keypoint wrist HPT model #668
• trainer: ddp_bf16_compress, DDP with a bf16 gradient all-reduce #667
• eval: overlay videos play at the source rate under DDP #666
• hpt: build the policy on CPU and let Lightning place it #664 : 1 other dependent PR (#665 )
• hpt: never re-initialise pretrained stems; hash-check them; mask text padding #645 : 1 other dependent PR (#646 )
• obs: proprio history (K proprio steps, one token each, learned time embedding) #650
• loss: mask the repeat-last action padding out of the HPT loss #649
• resnet: keep a frozen backbone in eval mode; give backbones their own LR group #652
• augs: per-sample image augmentation for HPT train-mode augs #651
• eval: deterministic validation (seeded prompts + flow sampling); log-only grad-norm MAD #644
• data: mecka_fold_flagship_top3_hpt_6d, the 3-operator flagship train split #643
• val: subsampled metric loaders + pinned contiguous video loaders #642
• infra: mecka 6D / keypoint recipes, 300M models, defaults, and the rollout revert #640
• eval: layout-aware frame-invariant metrics, and a train-split viz head #659
• data: stop letting one bad episode or a rotation channel take a run down #658
• norm: guard degenerate channels, horizon pooling, and no rot6d normalization #657
• keypoints: the 144-D wrist-first hand-keypoint action becomes the human default #656
• mecka: fix the double-mirrored LEFT wrist frame, and correct existing zarrs #655
• 6d: PI goes 6D-only; Eva and Human gain the continuous-rotation modes #654
• 6d: continuous rotation representation, bimanual layouts, shared norm helpers #653
• train: give each Slurm slurm job gets its own cache dir to avoid conflict #638 : 1 other dependent PR (#639 )
• write git version into the run dir for reproducibility #637
• train: don't load PI base weights on resume and verify that checkpoint path exists #636
• configs: declare proprio and actions dims once per HPT model config #634 : 1 other dependent PR (#635 )
• cache norm stats for data configs to minimize recomputation #633
• import cloud packages lazily to support local training #631 : 1 other dependent PR (#632 )
• cache dataset resolver for faster training initialization #630
• validate episode hashes in data config before training #628
• modify pi camera map to match data #629
• add tests to ensure training works between every model and data vendor #626
• run CI tests on every PR #625
• add env variables for user-specific paths #624
• build: set pyproject and uv as the dependency managers #623
• fix(configs): fix paths for data hydra yaml configs #622
• refactor(algo): delete ACT #627 : 1 other dependent PR (#621 )
• tests: create dedicated test folder with unit and integration tests #620
• add pyright type checker #619
• main

This stack of pull requests is managed by Graphite. Learn more about stacking.