trying out to re-init the action head to avoid pretraining dimension mismatch

src/lerobot/policies/vla_jepa/configuration_vla_jepa.py

@@ -27,6 +27,7 @@ class VLAJEPAConfig(PreTrainedConfig):
     jepa_encoder_name: str = "facebook/vjepa2-vitl-fpc64-256"
     freeze_qwen: bool = False
     enable_world_model: bool = True
+    reinit_action_head: bool = False
 
     tokenizer_padding_side: str = "left"
     prompt_template: str = "Your task is {instruction}. Infer the temporal dynamics from frames {actions} and produce the corresponding policy actions {e_actions}."

src/lerobot/policies/vla_jepa/modeling_vla_jepa.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 from collections import deque
 from pathlib import Path
 from typing import TYPE_CHECKING
@@ -554,3 +555,35 @@ class VLAJEPAPolicy(PreTrainedPolicy):
         **kwargs,
     ):
         return super().from_pretrained(pretrained_name_or_path, **kwargs)
+
+    @classmethod
+    def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
+        if not model.config.reinit_action_head:
+            return super()._load_as_safetensor(model, model_file, map_location, strict)
+
+        from safetensors.torch import load_file
+
+        state_dict = load_file(model_file, device=map_location)
+        current = model.state_dict()
+
+        mismatched: list[str] = []
+        filtered: dict = {}
+        for key, value in state_dict.items():
+            if key in current and value.shape != current[key].shape:
+                mismatched.append(
+                    f"{key}: checkpoint {tuple(value.shape)} vs model {tuple(current[key].shape)}"
+                )
+            else:
+                filtered[key] = value
+
+        if mismatched:
+            logging.warning(
+                f"reinit_action_head=True: skipping {len(mismatched)} tensor(s) with mismatched shapes "
+                f"(randomly re-initialised):\n  " + "\n  ".join(mismatched)
+            )
+
+        from lerobot.policies.utils import log_model_loading_keys
+
+        missing_keys, unexpected_keys = model.load_state_dict(filtered, strict=False)
+        log_model_loading_keys(missing_keys, unexpected_keys)
+        return model