* refactor(training): rename eval_freq to env_eval_freq
- Rename eval_freq to env_eval_freq to distinguish sim environment evaluation from offline loss evaluation.
* feat(training): add inline offline validation with train/eval split
- Add eval_split config for balanced per-task holdout
- Add eval_steps for periodic inline eval loss computation
- Add max_eval_samples to cap eval cost
* fix(datasets): remap absolute indices in __getitem__ for filtered datasets
* fix(train): vectorize eval subset selection for max_eval_samples
* fix(datasets): Move the remapping into EpisodeAwareSampler via absolute_to_relative_idx
* fix(validation): add eval_split range check and eval_steps warning
Validate eval_split is in [0.0, 1.0) to prevent garbage splits from
out-of-range values. Raise when eval_steps > 0 but eval_split is 0.0
since no offline eval will run.
* fix(train): prepare eval dataloader with accelerator for multi-GPU
Prepare eval_dataloader through accelerator.prepare() so eval data is
sharded across ranks instead of duplicated. Reduce eval_loss across
ranks with mean reduction for consistent logging.
* fix(test): rename eval_freq to env_eval_freq for multi-GPU training
Khalil Meftah
6a788fbdb0