diff --git a/examples/bumpy_hpo.yaml b/examples/bumpy_hpo.yaml
index bd0d109..48f53a1 100644
--- a/examples/bumpy_hpo.yaml
+++ b/examples/bumpy_hpo.yaml
@@ -11,6 +11,11 @@ metrics:
 static:
   global-mu: "23 42 66"
 
+wlm:
+  gpu-count: 0
+  cpu-count: 2
+  mem-gb: 4
+
 hpo:
   x:
     type: float
diff --git a/examples/wlm_plugins/ccc_plugin.sh b/examples/wlm_plugins/ccc_plugin.sh
index 14f1d5f..ec750f8 100755
--- a/examples/wlm_plugins/ccc_plugin.sh
+++ b/examples/wlm_plugins/ccc_plugin.sh
@@ -1,76 +1,68 @@
 #!/usr/bin/env bash
 # =============================================================================
-# Trial script for iterate2  -  CCC (IBM Spectrum LSF) backend
+# iterate2 WLM plugin  -  CCC (IBM Spectrum LSF)
 #
-# Called once per Optuna trial.  Activates the venv, builds the training
-# command from ITERATE_PARAM_* env vars, and submits it via bsub -K.
+# Invocation contract (set by iterate2 when --wlm-plugin is used):
 #
-# Environment variables provided by iterate2
-# ------------------------------------------
-#   ITERATE_TRIAL_NUMBER   integer trial ID
-#   ITERATE_OUT_FILE       metric lines are read from here by iterate2
-#   ITERATE_ERR_FILE       path for error output
-#   ITERATE_PARAM_<KEY>    one variable per HPO + static parameter
-#                          (key uppercased, hyphens -> underscores)
+#   ccc_plugin.sh <script> [--<wlm-key> <value>]...
 #
-# Path overrides (set in the run script or your environment)
-# ----------------------------------------------------------
-#   GRIDFM_ROOT   repo root          (default below)
-#   GRIDFM_VENV   Python venv path   (default below)
-#   CUDA_BASE     CUDA install root  (default below)
+# where <script> is the executable passed as --script, and the --<wlm-key>
+# flags are the keys of the YAML 'wlm:' section, e.g.
+#   --gpu-count 1 --cpu-count 16 --mem-gb 32 \
+#   --lsf-gpu-config "num=1:mode=exclusive_process:mps=no"
+#
+# iterate2 also exports the per-trial env vars (ITERATE_TRIAL_NUMBER,
+# ITERATE_OUT_FILE, ITERATE_ERR_FILE, ITERATE_PARAM_*) which bsub forwards
+# to the job so the wrapped <script> can read them.
 # =============================================================================
 
 set -euo pipefail
 
-# -- iterate2 standard vars ---------------------------------------------------
+SCRIPT="${1:?usage: ccc_plugin.sh <script> [--key value]...}"
+shift
+
+# Defaults; overridden by --<key> <value> argv pairs below.
+GPU_COUNT=1
+CPU_COUNT=4
+MEM_GB=16
+LSF_GPU_CONFIG=""
+QUEUE=""
+
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --gpu-count)       GPU_COUNT="$2";       shift 2 ;;
+    --cpu-count)       CPU_COUNT="$2";       shift 2 ;;
+    --mem-gb)          MEM_GB="$2";          shift 2 ;;
+    --lsf-gpu-config)  LSF_GPU_CONFIG="$2";  shift 2 ;;
+    --queue)           QUEUE="$2";           shift 2 ;;
+    *) echo "ccc_plugin: ignoring unknown flag $1 $2" >&2; shift 2 ;;
+  esac
+done
+
 TRIAL_NUMBER="${ITERATE_TRIAL_NUMBER:?ITERATE_TRIAL_NUMBER not set}"
 OUT_FILE="${ITERATE_OUT_FILE:?ITERATE_OUT_FILE not set}"
 ERR_FILE="${ITERATE_ERR_FILE:?ITERATE_ERR_FILE not set}"
 
-# -- Paths (override via env) -------------------------------------------------
-GRIDFM_ROOT="${GRIDFM_ROOT:-/dccstor/terratorch/users/rkie/gitco/gridfm-graphkit}"
-GRIDFM_VENV="${GRIDFM_VENV:-/u/rkie/venvs/venv_gridfm-graphkit}"
-CUDA_BASE="${CUDA_BASE:-/opt/share/cuda-12.8.1}"
-
-# -- LSF resources ------------------------------------------------------------
-# GPU_COUNT comes from the HPO group param so LSF allocates the right number.
-GPU_COUNT="${ITERATE_PARAM_GPU_NUM:-1}"
-CPU_COUNT=16
-MEM_GB=32
 MEM_MB=$(( MEM_GB * 1024 ))
-GPU_STRING="num=${GPU_COUNT}:mode=exclusive_process:mps=no:gmodel=NVIDIAA100_SXM4_80GB"
-# QUEUE="normal"   # uncomment to target a specific queue
+GPU_STRING="${LSF_GPU_CONFIG:-num=${GPU_COUNT}:mode=exclusive_process:mps=no}"
 
-# -- Build training command from ITERATE_PARAM_* vars -------------------------
-# --gpu_num is NOT a gridfm_graphkit flag; GPU count is controlled via bsub -gpu.
-TRAIN_CMD="gridfm_graphkit train"
-TRAIN_CMD+=" --batch_size ${ITERATE_PARAM_BATCH_SIZE}"
-TRAIN_CMD+=" --num_workers ${ITERATE_PARAM_NUM_WORKERS}"
-TRAIN_CMD+=" --config     ${ITERATE_PARAM_CONFIG}"
-TRAIN_CMD+=" --data_path  ${ITERATE_PARAM_DATA_PATH}"
-# [[ -n "${ITERATE_PARAM_COMPILE:-}" ]] && TRAIN_CMD+=" --compile ${ITERATE_PARAM_COMPILE}"
+QUEUE_FLAG=()
+[[ -n "$QUEUE" ]] && QUEUE_FLAG=(-q "$QUEUE")
 
-# -- Compose full job shell command -------------------------------------------
-JOB_CMD="\
-export PATH='${CUDA_BASE}/bin:\$PATH' && \
-export CUDA_HOME='${CUDA_BASE}' && \
-export LD_LIBRARY_PATH='${CUDA_BASE}/lib64:\$LD_LIBRARY_PATH' && \
-cd '${GRIDFM_ROOT}' && \
-source '${GRIDFM_VENV}/bin/activate' && \
-${TRAIN_CMD}"
+GPU_FLAG=()
+if [[ "$GPU_COUNT" -gt 0 || -n "$LSF_GPU_CONFIG" ]]; then
+  GPU_FLAG=(-gpu "$GPU_STRING")
+fi
 
-# -- Submit via bsub ----------------------------------------------------------
-# -K  : blocks until the job completes (iterate2 runs each trial in a thread)
-# -n  : CPU slots
-# -o/-e: redirect job stdout/stderr to paths iterate2 will read metrics from
 bsub \
   -K \
-  -gpu  "${GPU_STRING}" \
+  "${GPU_FLAG[@]}" \
+  "${QUEUE_FLAG[@]}" \
   -n    "${CPU_COUNT}" \
   -R    "rusage[mem=${MEM_MB}]" \
   -o    "${OUT_FILE}" \
   -e    "${ERR_FILE}" \
   -J    "hpo_trial_${TRIAL_NUMBER}" \
-  "${JOB_CMD}"
+  "${SCRIPT}"
 
 echo "[ccc_plugin] trial ${TRIAL_NUMBER} finished"
diff --git a/terratorch_iterate/iterate2/_iterate2.py b/terratorch_iterate/iterate2/_iterate2.py
index 8d2e8cf..48e950e 100644
--- a/terratorch_iterate/iterate2/_iterate2.py
+++ b/terratorch_iterate/iterate2/_iterate2.py
@@ -68,8 +68,12 @@ def parse_args():
     )
     p.add_argument("--script",            required=True,
                    help="Executable to call for each trial")
+    p.add_argument("--wlm-plugin",        default=None,
+                   help="Optional wrapper executable. When set, iterate2 invokes "
+                        "'<wlm-plugin> <script> [--<wlm-key> <value>]...' per trial, "
+                        "where the wlm keys come from the YAML 'wlm:' section.")
     p.add_argument("--hpo-yaml",          required=True,
-                   help="YAML file with 'hpo:', 'static:', and 'metrics:' sections")
+                   help="YAML file with 'hpo:', 'static:', 'metrics:', and optional 'wlm:' sections")
     p.add_argument("--optuna-study-name", required=True)
     p.add_argument("--optuna-db-path",    required=True,
                    help="Optuna storage URL (sqlite:///hpo.db, js:///journal.log, postgresql://…)")
@@ -97,6 +101,13 @@ def load_static(data: dict) -> dict:
     logger.info("Static params: %d key(s): %s", len(static), list(static.keys()))
     return static
 
+def load_wlm(data: dict) -> dict:
+    wlm = data.get("wlm", {}) or {}
+    if wlm:
+        logger.info("WLM config: %d key(s): %s", len(wlm), list(wlm.keys()))
+    return wlm
+
+
 def load_metrics(data: dict, fallback: str = "score") -> List[str]:
     raw = data.get("metrics", None)
     if raw is None:
@@ -180,11 +191,19 @@ def _stream(pipe, dest_file: str, trial_id: int, dest_stream):
                 dest_stream.write(f"{prefix} {line}")
                 dest_stream.flush()
 
-def run_script(script: str, env: dict, trial_id: int, out_file: str, err_file: str):
-    """Run *script* with *env*, stream output, raise on non-zero exit."""
-    logger.info("Trial %d: calling %s", trial_id, script)
+def run_script(script: str, env: dict, trial_id: int, out_file: str, err_file: str,
+               wlm_plugin: Optional[str] = None, wlm_config: Optional[dict] = None):
+    """Run *script* (or *wlm_plugin* wrapping it) with *env*, stream output."""
+    if wlm_plugin:
+        argv = [wlm_plugin, script]
+        for k, v in (wlm_config or {}).items():
+            argv.extend([f"--{k}", str(v)])
+        logger.info("Trial %d: calling plugin %s with argv %s", trial_id, wlm_plugin, argv)
+    else:
+        argv = [script]
+        logger.info("Trial %d: calling %s", trial_id, script)
     proc = subprocess.Popen(
-        [script], env=env,
+        argv, env=env,
         stdout=subprocess.PIPE, stderr=subprocess.PIPE,
     )
     import threading as _t
@@ -219,6 +238,7 @@ def main():
     data       = load_yaml(args.hpo_yaml)
     hpo_space  = load_hpo_space(data)
     static     = load_static(data)
+    wlm_config = load_wlm(data)
     metrics    = load_metrics(data)
     directions = ["maximize"] * len(metrics)
     logger.info("Metrics: %s", metrics)
@@ -271,8 +291,9 @@ def objective(trial):
             env_key = "ITERATE_PARAM_" + str(k).upper().replace("-", "_").replace(" ", "_")
             env[env_key] = str(v) if v is not None else ""
 
-        # ── Call the script ───────────────────────────────────────────────
-        run_script(args.script, env, trial.number, out_file, err_file)
+        # ── Call the script (optionally wrapped by --wlm-plugin) ──────────
+        run_script(args.script, env, trial.number, out_file, err_file,
+                   wlm_plugin=args.wlm_plugin, wlm_config=wlm_config)
 
         # ── Extract metrics ───────────────────────────────────────────────
         values = extract_metrics(out_file, err_file, metrics)