feat(rl): consolidate HIL-SERL checkpoint into HF-style components

Make `RLAlgorithmConfig` and `RLAlgorithm` `HubMixin`s, add abstract
`state_dict()` / `load_state_dict()` for critic ensemble, target nets
and `log_alpha`, and persist them as a sibling `algorithm/` component
next to `pretrained_model/`. Replace the pickled `training_state.pt`
with an enriched `training_step.json` carrying `step` and
`interaction_step`, so resume restores actor + critics + target nets +
temperature + optimizers + RNG + counters from HF-standard files.

tests/rl/test_trainer.py

@@ -68,6 +68,12 @@ class _DummyRLAlgorithm(RLAlgorithm):
     def load_weights(self, weights, device="cpu") -> None:
         _ = (weights, device)
 
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        return {}
+
+    def load_state_dict(self, state_dict, device="cpu") -> None:
+        _ = (state_dict, device)
+
 
 class _SimpleMixer:
     def get_iterator(self, batch_size: int, async_prefetch: bool = True, queue_size: int = 2):