fix: resolve audited training and inference bugs

- reject prompts that encode to zero tokens in add_task instead of admitting a task whose prefill can never run, and surface empty-id run_batch calls as prompt_empty errors
- deliver the STOP stream callback when cancelling a live task so clients observe termination instead of hanging until socket timeout
- strip the torch.compile _orig_mod. prefix at every unwrap_model site and when loading checkpoints so FSDP state dicts and saved weights no longer leak the wrapper name into downstream keys
- reject online_* train strategies with nprocs > 1 at config validation time, explaining the NCCL all-gather deadlock they would otherwise hit mid-run
- apply the frequency penalty before temperature scaling (OpenAI semantics) so the penalty survives temperature=0 instead of being annihilated by the 1e8 logit blowup, and exclude penalty pipelines from the greedy fast path
- return logprobs from the raw pre-strategy distribution so they match training-side policy logprobs for PPO/GRPO importance ratios

astrai/inference/scheduler.py

@@ -444,6 +444,10 @@ class InferenceScheduler:
         tasks: List[Optional[Task]] = []
         error_reasons: List[Optional[str]] = []
         for ids in prompt_ids_list:
+            if not ids:
+                tasks.append(None)
+                error_reasons.append("prompt_empty")
+                continue
             if len(ids) >= seq_cap:
                 tasks.append(None)
                 error_reasons.append("prompt_too_long")