Fix remote training and model validation pipeline

tools/windows_training_agent.py

@@ -64,19 +64,20 @@ def poll_once(args: argparse.Namespace, repo_root: Path, runtime_dir: Path, log_
     try:
         run_retrain(args, job_id, job, repo_root, log_path)
         summary = read_json(runtime_dir / "torch_retrain_guard.json")
-        if summary.get("accepted") is not True:
-            raise RuntimeError(
-                "candidate rejected by untouched-holdout guard: "
-                + str(summary.get("reason") or "validation failed")
-            )
-        report_progress(args, job_id, "running", "uploading", 72, "Обучение завершено, загружаю артефакты")
-        for name in ARTIFACT_NAMES:
-            path = runtime_dir / name
-            if path.is_file():
-                upload_artifact(args, job_id, path, log_path)
+        accepted = summary.get("accepted") is True
+        if accepted:
+            report_progress(args, job_id, "running", "uploading", 72, "Обучение завершено, загружаю артефакты")
+            for name in ARTIFACT_NAMES:
+                path = runtime_dir / name
+                if path.is_file():
+                    upload_artifact(args, job_id, path, log_path)
+            message = "training completed; candidate accepted"
+            log(log_path, f"Completed retrain job {job_id}; candidate accepted")
+        else:
+            reason = str(summary.get("reason") or "validation failed")
+            message = f"training completed; candidate rejected by quality gate: {reason}"
+            log(log_path, f"Completed retrain job {job_id}; candidate rejected: {reason}")
         success = True
-        message = "training completed"
-        log(log_path, f"Completed retrain job {job_id}")
     except Exception as exc:  # noqa: BLE001 - report failure to the bot.
         message = str(exc)
         log(log_path, f"Job {job_id} failed: {message}")
@@ -108,6 +109,10 @@ def run_retrain(args: argparse.Namespace, job_id: str, job: dict[str, Any], repo
         "dropouts": "-Dropouts",
         "epochs": "-Epochs",
         "holdout_window": "-HoldoutWindow",
+        "ensemble_seeds": "-EnsembleSeeds",
+        "selection_folds": "-SelectionFolds",
+        "learning_rate": "-LearningRate",
+        "weight_decay": "-WeightDecay",
     }
     for key, ps_arg in arg_map.items():
         value = parameters.get(key)