* fix(datasets): expose a generator on EpisodeAwareSampler for distributed shuffle sync
In distributed training, accelerate can only synchronize the shuffle
permutation across ranks when the sampler exposes a generator attribute.
EpisodeAwareSampler shuffled via the global torch RNG, so disjoint batch
shards relied on every rank's global CPU RNG staying in lockstep forever;
any rank-asymmetric RNG consumption (e.g. eval rollouts on the main
process only) silently desynced the permutations and ranks trained on
overlapping/missing samples.
* fix(train): seed sampler generator and gate dataset download per node
- Pass a generator seeded with cfg.seed to EpisodeAwareSampler so
accelerator.prepare registers it as the synchronized RNG and the
shuffle order is reproducible.
- Gate the initial make_dataset call on is_local_main_process instead of
is_main_process: the global main process only exists on node 0, so on
every other node all local ranks were downloading the dataset and
building the Arrow cache concurrently.
Pepijn