- Record batch_size in training_step.json alongside num_processes and feed
the checkpoint's value into compute_sampler_state on resume; warn when it
differs (per-rank sample-exactness needs the same batch size).
- Document the set_epoch vs __iter__ auto-advance coupling on EpisodeAwareSampler
(callers should rely on exactly one mechanism per run).
- Note the broadened (reproducibility-breaking) sampler guard and the no-generator
distributed sharding correctness in lerobot_train.py.
- Add load_training_batch_size + parallel tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
pepijn
3f6909fb63