diff --git a/torchrl/trainers/algorithms/configs/__init__.py b/torchrl/trainers/algorithms/configs/__init__.py
index f76b83b1387..ca322c84938 100644
--- a/torchrl/trainers/algorithms/configs/__init__.py
+++ b/torchrl/trainers/algorithms/configs/__init__.py
@@ -85,6 +85,7 @@
     ValueModelConfig,
 )
 from torchrl.trainers.algorithms.configs.objectives import (
+    GAEConfig,
     HardUpdateConfig,
     LossConfig,
     PPOLossConfig,
@@ -126,6 +127,7 @@
     InitTrackerConfig,
     KLRewardTransformConfig,
     LineariseRewardsConfig,
+    ModuleTransformConfig,
     MultiActionConfig,
     MultiStepTransformConfig,
     NoopResetEnvConfig,
@@ -179,6 +181,18 @@
     SGDConfig,
     SparseAdamConfig,
 )
+from torchrl.trainers.algorithms.configs.weight_sync_schemes import (
+    DistributedWeightSyncSchemeConfig,
+    MultiProcessWeightSyncSchemeConfig,
+    NoWeightSyncSchemeConfig,
+    RayModuleTransformSchemeConfig,
+    RayWeightSyncSchemeConfig,
+    RPCWeightSyncSchemeConfig,
+    SharedMemWeightSyncSchemeConfig,
+    VLLMDoubleBufferSyncSchemeConfig,
+    VLLMWeightSyncSchemeConfig,
+    WeightSyncSchemeConfig,
+)
 from torchrl.trainers.algorithms.configs.weight_update import (
     DistributedWeightUpdaterConfig,
     MultiProcessedWeightUpdaterConfig,
@@ -273,6 +287,7 @@
     "InitTrackerConfig",
     "KLRewardTransformConfig",
     "LineariseRewardsConfig",
+    "ModuleTransformConfig",
     "MultiActionConfig",
     "MultiStepTransformConfig",
     "NoopResetEnvConfig",
@@ -330,6 +345,8 @@
     "LossConfig",
     "PPOLossConfig",
     "SACLossConfig",
+    # Value functions
+    "GAEConfig",
     # Trainers
     "PPOTrainerConfig",
     "SACTrainerConfig",
@@ -348,6 +365,17 @@
     "RPCWeightUpdaterConfig",
     "DistributedWeightUpdaterConfig",
     "vLLMUpdaterConfig",
+    # Weight Sync Schemes
+    "WeightSyncSchemeConfig",
+    "MultiProcessWeightSyncSchemeConfig",
+    "SharedMemWeightSyncSchemeConfig",
+    "NoWeightSyncSchemeConfig",
+    "RayWeightSyncSchemeConfig",
+    "RayModuleTransformSchemeConfig",
+    "RPCWeightSyncSchemeConfig",
+    "DistributedWeightSyncSchemeConfig",
+    "VLLMWeightSyncSchemeConfig",
+    "VLLMDoubleBufferSyncSchemeConfig",
 ]
 
 
@@ -356,6 +384,10 @@ def _register_configs():
 
     This function is called lazily to avoid GlobalHydra initialization issues
     during testing. It should be called explicitly when needed.
+
+    To add a new config:
+    - Write the config class in the appropriate file (e.g. torchrl/trainers/algorithms/configs/transforms.py) and add it to the __all__ list in torchrl/trainers/algorithms/configs/__init__.py
+    - Register the config in the appropriate group, e.g. cs.store(group="transform", name="new_transform", node=NewTransformConfig)
     """
     cs = ConfigStore.instance()
 
@@ -461,6 +493,7 @@ def _register_configs():
     cs.store(group="transform", name="action_discretizer", node=ActionDiscretizerConfig)
     cs.store(group="transform", name="traj_counter", node=TrajCounterConfig)
     cs.store(group="transform", name="linearise_rewards", node=LineariseRewardsConfig)
+    cs.store(group="transform", name="module", node=ModuleTransformConfig)
     cs.store(group="transform", name="conditional_skip", node=ConditionalSkipConfig)
     cs.store(group="transform", name="multi_action", node=MultiActionConfig)
     cs.store(group="transform", name="timer", node=TimerConfig)
@@ -487,6 +520,7 @@ def _register_configs():
     cs.store(group="transform", name="vip", node=VIPTransformConfig)
     cs.store(group="transform", name="vip_reward", node=VIPRewardTransformConfig)
     cs.store(group="transform", name="vec_norm_v2", node=VecNormV2Config)
+    cs.store(group="transform", name="module", node=ModuleTransformConfig)
 
     # =============================================================================
     # Loss Configurations
@@ -496,6 +530,16 @@ def _register_configs():
     cs.store(group="loss", name="ppo", node=PPOLossConfig)
     cs.store(group="loss", name="sac", node=SACLossConfig)
 
+    # =============================================================================
+    # Value Function Configurations
+    # =============================================================================
+
+    cs.store(group="value", name="gae", node=GAEConfig)
+
+    # =============================================================================
+    # Target Net Updater Configurations
+    # =============================================================================
+
     cs.store(group="target_net_updater", name="soft", node=SoftUpdateConfig)
     cs.store(group="target_net_updater", name="hard", node=HardUpdateConfig)
 
@@ -595,6 +639,41 @@ def _register_configs():
     )
     cs.store(group="weight_updater", name="vllm", node=vLLMUpdaterConfig)
 
+    # =============================================================================
+    # Weight Sync Scheme Configurations
+    # =============================================================================
+
+    cs.store(group="weight_sync_scheme", name="base", node=WeightSyncSchemeConfig)
+    cs.store(
+        group="weight_sync_scheme",
+        name="multiprocess",
+        node=MultiProcessWeightSyncSchemeConfig,
+    )
+    cs.store(
+        group="weight_sync_scheme",
+        name="shared_mem",
+        node=SharedMemWeightSyncSchemeConfig,
+    )
+    cs.store(group="weight_sync_scheme", name="no_sync", node=NoWeightSyncSchemeConfig)
+    cs.store(group="weight_sync_scheme", name="ray", node=RayWeightSyncSchemeConfig)
+    cs.store(
+        group="weight_sync_scheme",
+        name="ray_module_transform",
+        node=RayModuleTransformSchemeConfig,
+    )
+    cs.store(group="weight_sync_scheme", name="rpc", node=RPCWeightSyncSchemeConfig)
+    cs.store(
+        group="weight_sync_scheme",
+        name="distributed",
+        node=DistributedWeightSyncSchemeConfig,
+    )
+    cs.store(group="weight_sync_scheme", name="vllm", node=VLLMWeightSyncSchemeConfig)
+    cs.store(
+        group="weight_sync_scheme",
+        name="vllm_double_buffer",
+        node=VLLMDoubleBufferSyncSchemeConfig,
+    )
+
 
 if not sys.version_info < (3, 10):  #  type: ignore # noqa
     _register_configs()
diff --git a/torchrl/trainers/algorithms/configs/collectors.py b/torchrl/trainers/algorithms/configs/collectors.py
index 9e57b7c19fa..f90f5b6f6b9 100644
--- a/torchrl/trainers/algorithms/configs/collectors.py
+++ b/torchrl/trainers/algorithms/configs/collectors.py
@@ -51,7 +51,9 @@ class SyncDataCollectorConfig(DataCollectorConfig):
     cudagraph_policy: Any = None
     no_cuda_sync: bool = False
     weight_updater: Any = None
+    weight_sync_schemes: Any = None
     track_policy_version: bool = False
+    local_init_rb: bool = False
     _target_: str = "torchrl.collectors.SyncDataCollector"
     _partial_: bool = False
 
@@ -94,7 +96,9 @@ class AsyncDataCollectorConfig(DataCollectorConfig):
     cudagraph_policy: Any = None
     no_cuda_sync: bool = False
     weight_updater: Any = None
+    weight_sync_schemes: Any = None
     track_policy_version: bool = False
+    local_init_rb: bool = False
     _target_: str = "torchrl.collectors.aSyncDataCollector"
     _partial_: bool = False
 
@@ -136,7 +140,9 @@ class MultiSyncDataCollectorConfig(DataCollectorConfig):
     cudagraph_policy: Any = None
     no_cuda_sync: bool = False
     weight_updater: Any = None
+    weight_sync_schemes: Any = None
     track_policy_version: bool = False
+    local_init_rb: bool = False
     _target_: str = "torchrl.collectors.MultiSyncDataCollector"
     _partial_: bool = False
 
@@ -179,7 +185,9 @@ class MultiaSyncDataCollectorConfig(DataCollectorConfig):
     cudagraph_policy: Any = None
     no_cuda_sync: bool = False
     weight_updater: Any = None
+    weight_sync_schemes: Any = None
     track_policy_version: bool = False
+    local_init_rb: bool = False
     _target_: str = "torchrl.collectors.MultiaSyncDataCollector"
     _partial_: bool = False
 
diff --git a/torchrl/trainers/algorithms/configs/data.py b/torchrl/trainers/algorithms/configs/data.py
index 08a8eb44cc3..f840c52145b 100644
--- a/torchrl/trainers/algorithms/configs/data.py
+++ b/torchrl/trainers/algorithms/configs/data.py
@@ -254,6 +254,7 @@ class LazyMemmapStorageConfig(StorageConfig):
     device: Any = None
     ndim: int = 1
     compilable: bool = False
+    shared_init: bool = False
 
 
 @dataclass
@@ -265,6 +266,7 @@ class LazyTensorStorageConfig(StorageConfig):
     device: Any = None
     ndim: int = 1
     compilable: bool = False
+    shared_init: bool = False
 
 
 @dataclass
diff --git a/torchrl/trainers/algorithms/configs/modules.py b/torchrl/trainers/algorithms/configs/modules.py
index 8ec1a4df984..189d47c2561 100644
--- a/torchrl/trainers/algorithms/configs/modules.py
+++ b/torchrl/trainers/algorithms/configs/modules.py
@@ -202,6 +202,7 @@ class ModelConfig(ConfigBase):
     _partial_: bool = False
     in_keys: Any = None
     out_keys: Any = None
+    shared: bool = False
 
     def __post_init__(self) -> None:
         """Post-initialization hook for model configurations."""
@@ -226,7 +227,7 @@ class TensorDictModuleConfig(ModelConfig):
 
     def __post_init__(self) -> None:
         """Post-initialization hook for TensorDict module configurations."""
-        super().__post_init__()
+        return super().__post_init__()
 
 
 @dataclass
@@ -312,6 +313,7 @@ def _make_tanh_normal_model(*args, **kwargs):
     return_log_prob = kwargs.pop("return_log_prob", False)
     eval_mode = kwargs.pop("eval_mode", False)
     exploration_type = kwargs.pop("exploration_type", "RANDOM")
+    shared = kwargs.pop("shared", False)
 
     # Now instantiate the network
     if hasattr(network, "_target_"):
@@ -328,6 +330,8 @@ def _make_tanh_normal_model(*args, **kwargs):
         )
 
     module = TensorDictModule(network, in_keys=in_keys, out_keys=param_keys)
+    if shared:
+        module = module.share_memory()
 
     # Create ProbabilisticTensorDictModule
     prob_module = ProbabilisticTensorDictModule(
@@ -350,4 +354,7 @@ def _make_value_model(*args, **kwargs):
     from torchrl.modules import ValueOperator
 
     network = kwargs.pop("network")
+    shared = kwargs.pop("shared", False)
+    if shared:
+        network = network.share_memory()
     return ValueOperator(network, **kwargs)
diff --git a/torchrl/trainers/algorithms/configs/objectives.py b/torchrl/trainers/algorithms/configs/objectives.py
index a0d1c8fb0d3..6be1bc845b9 100644
--- a/torchrl/trainers/algorithms/configs/objectives.py
+++ b/torchrl/trainers/algorithms/configs/objectives.py
@@ -148,3 +148,30 @@ class HardUpdateConfig(TargetNetUpdaterConfig):
 
     _target_: str = "torchrl.objectives.utils.HardUpdate."
     value_network_update_interval: int = 1000
+
+
+@dataclass
+class GAEConfig(LossConfig):
+    """A class to configure a GAELoss."""
+
+    gamma: float | None = None
+    lmbda: float | None = None
+    value_network: Any = None
+    average_gae: bool = True
+    differentiable: bool = False
+    vectorized: bool | None = None
+    skip_existing: bool | None = None
+    advantage_key: str | None = None
+    value_target_key: str | None = None
+    value_key: str | None = None
+    shifted: bool = False
+    device: Any = None
+    time_dim: int | None = None
+    auto_reset_env: bool = False
+    deactivate_vmap: bool = False
+    _target_: str = "torchrl.objectives.value.GAE"
+    _partial_: bool = False
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for GAELoss configurations."""
+        super().__post_init__()
diff --git a/torchrl/trainers/algorithms/configs/trainers.py b/torchrl/trainers/algorithms/configs/trainers.py
index a35e686d124..fce985f4557 100644
--- a/torchrl/trainers/algorithms/configs/trainers.py
+++ b/torchrl/trainers/algorithms/configs/trainers.py
@@ -9,10 +9,12 @@
 from typing import Any
 
 import torch
+from tensordict.nn import TensorDictModuleBase
 
 from torchrl.collectors import DataCollectorBase
 from torchrl.objectives.common import LossModule
 from torchrl.objectives.utils import TargetNetUpdater
+from torchrl.objectives.value.advantages import GAE
 from torchrl.trainers.algorithms.configs.common import ConfigBase
 from torchrl.trainers.algorithms.ppo import PPOTrainer
 from torchrl.trainers.algorithms.sac import SACTrainer
@@ -54,6 +56,7 @@ class SACTrainerConfig(TrainerConfig):
     critic_network: Any = None
     target_net_updater: Any = None
     async_collection: bool = False
+    log_timings: bool = False
 
     _target_: str = "torchrl.trainers.algorithms.configs.trainers._make_sac_trainer"
 
@@ -87,6 +90,7 @@ def _make_sac_trainer(*args, **kwargs) -> SACTrainer:
     kwargs.pop("create_env_fn")
     target_net_updater = kwargs.pop("target_net_updater")
     async_collection = kwargs.pop("async_collection", False)
+    log_timings = kwargs.pop("log_timings", False)
 
     # Instantiate networks first
     if actor_network is not None:
@@ -152,6 +156,7 @@ def _make_sac_trainer(*args, **kwargs) -> SACTrainer:
         replay_buffer=replay_buffer,
         target_net_updater=target_net_updater,
         async_collection=async_collection,
+        log_timings=log_timings,
     )
 
 
@@ -161,6 +166,37 @@ class PPOTrainerConfig(TrainerConfig):
 
     This class defines the configuration parameters for creating a PPO trainer,
     including both required and optional fields with sensible defaults.
+
+    Args:
+        collector: The data collector for gathering training data.
+        total_frames: Total number of frames to train for.
+        optim_steps_per_batch: Number of optimization steps per batch.
+        loss_module: The loss module for computing policy and value losses.
+        optimizer: The optimizer for training.
+        logger: Logger for tracking training metrics.
+        save_trainer_file: File path for saving trainer state.
+        replay_buffer: Replay buffer for storing data.
+        frame_skip: Frame skip value for the environment. Default: 1.
+        clip_grad_norm: Whether to clip gradient norms. Default: True.
+        clip_norm: Maximum gradient norm value.
+        progress_bar: Whether to show a progress bar. Default: True.
+        seed: Random seed for reproducibility.
+        save_trainer_interval: Interval for saving trainer state. Default: 10000.
+        log_interval: Interval for logging metrics. Default: 10000.
+        create_env_fn: Environment creation function.
+        actor_network: Actor network configuration.
+        critic_network: Critic network configuration.
+        num_epochs: Number of epochs per batch. Default: 4.
+        async_collection: Whether to use async collection. Default: False.
+        add_gae: Whether to add GAE computation. Default: True.
+        gae: Custom GAE module configuration.
+        weight_update_map: Mapping from collector destination paths to trainer source paths.
+            Required if collector has weight_sync_schemes configured.
+            Example: {"policy": "loss_module.actor_network",
+                     "replay_buffer.transforms[0]": "loss_module.critic_network"}
+        log_timings: Whether to automatically log timing information for all hooks.
+            If True, timing metrics will be logged to the logger (e.g., wandb, tensorboard)
+            with prefix "time/" (e.g., "time/hook/UpdateWeights"). Default: False.
     """
 
     collector: Any
@@ -183,6 +219,10 @@ class PPOTrainerConfig(TrainerConfig):
     critic_network: Any = None
     num_epochs: int = 4
     async_collection: bool = False
+    add_gae: bool = True
+    gae: Any = None
+    weight_update_map: dict[str, str] | None = None
+    log_timings: bool = False
 
     _target_: str = "torchrl.trainers.algorithms.configs.trainers._make_ppo_trainer"
 
@@ -213,7 +253,12 @@ def _make_ppo_trainer(*args, **kwargs) -> PPOTrainer:
     seed = kwargs.pop("seed")
     actor_network = kwargs.pop("actor_network")
     critic_network = kwargs.pop("critic_network")
+    add_gae = kwargs.pop("add_gae", True)
+    gae = kwargs.pop("gae")
     create_env_fn = kwargs.pop("create_env_fn")
+    weight_update_map = kwargs.pop("weight_update_map", None)
+    log_timings = kwargs.pop("log_timings", False)
+
     if create_env_fn is not None:
         # could be referenced somewhere else, no need to raise an error
         pass
@@ -225,6 +270,19 @@ def _make_ppo_trainer(*args, **kwargs) -> PPOTrainer:
         actor_network = actor_network()
     if critic_network is not None:
         critic_network = critic_network()
+    else:
+        critic_network = loss_module.critic_network
+
+    # Ensure GAE in replay buffer uses the same value network instance as loss module
+    # This fixes the issue where Hydra instantiates separate instances of value_model
+    if (
+        replay_buffer is not None
+        and hasattr(replay_buffer, "_transform")
+        and len(replay_buffer._transform) > 1
+        and hasattr(replay_buffer._transform[1], "module")
+        and hasattr(replay_buffer._transform[1].module, "value_network")
+    ):
+        replay_buffer._transform[1].module.value_network = critic_network
 
     if not isinstance(collector, DataCollectorBase):
         # then it's a partial config
@@ -258,6 +316,9 @@ def _make_ppo_trainer(*args, **kwargs) -> PPOTrainer:
         )
     if not isinstance(logger, Logger) and logger is not None:
         raise ValueError(f"logger must be a Logger, got {type(logger)}")
+    # instantiate gae if it is a partial config
+    if not isinstance(gae, (GAE, TensorDictModuleBase)) and gae is not None:
+        gae = gae()
 
     return PPOTrainer(
         collector=collector,
@@ -277,4 +338,8 @@ def _make_ppo_trainer(*args, **kwargs) -> PPOTrainer:
         replay_buffer=replay_buffer,
         num_epochs=num_epochs,
         async_collection=async_collection,
+        add_gae=add_gae,
+        gae=gae,
+        weight_update_map=weight_update_map,
+        log_timings=log_timings,
     )
diff --git a/torchrl/trainers/algorithms/configs/transforms.py b/torchrl/trainers/algorithms/configs/transforms.py
index 4a60e2da9b0..be221ec7934 100644
--- a/torchrl/trainers/algorithms/configs/transforms.py
+++ b/torchrl/trainers/algorithms/configs/transforms.py
@@ -938,3 +938,19 @@ class FlattenTensorDictConfig(TransformConfig):
     def __post_init__(self) -> None:
         """Post-initialization hook for FlattenTensorDict configuration."""
         super().__post_init__()
+
+
+@dataclass
+class ModuleTransformConfig(TransformConfig):
+    """Configuration for ModuleTransform."""
+
+    module: Any = None
+    device: Any = None
+    no_grad: bool = False
+    inverse: bool = False
+    _target_: str = "torchrl.envs.transforms.module.ModuleTransform"
+    _partial_: bool = False
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for ModuleTransform configuration."""
+        super().__post_init__()
diff --git a/torchrl/trainers/algorithms/configs/weight_sync_schemes.py b/torchrl/trainers/algorithms/configs/weight_sync_schemes.py
new file mode 100644
index 00000000000..4417e5c2cb3
--- /dev/null
+++ b/torchrl/trainers/algorithms/configs/weight_sync_schemes.py
@@ -0,0 +1,196 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from torchrl.trainers.algorithms.configs.common import ConfigBase
+
+
+@dataclass
+class WeightSyncSchemeConfig(ConfigBase):
+    """Base configuration for weight synchronization schemes."""
+
+    _target_: str = "torchrl.weight_update.WeightSyncScheme"
+    _partial_: bool = False
+
+    # Common argument for all schemes
+    strategy: str = "tensordict"  # "tensordict" or "state_dict"
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for weight sync scheme configurations."""
+
+
+@dataclass
+class MultiProcessWeightSyncSchemeConfig(ConfigBase):
+    """Configuration for MultiProcessWeightSyncScheme.
+
+    Weight synchronization for multiprocess operations using pipes.
+    This scheme creates transports that communicate via multiprocessing pipes.
+    """
+
+    _target_: str = "torchrl.weight_update.MultiProcessWeightSyncScheme"
+    _partial_: bool = False
+
+    strategy: str = "tensordict"  # "tensordict" or "state_dict"
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for multiprocess weight sync scheme configurations."""
+
+
+@dataclass
+class SharedMemWeightSyncSchemeConfig(ConfigBase):
+    """Configuration for SharedMemWeightSyncScheme.
+
+    Weight synchronization using shared memory for in-place weight updates.
+    Workers automatically see weight updates without explicit message passing.
+
+    By default, uses lazy registration (auto_register=True) which makes it seamless
+    to use with Hydra configs - models are automatically registered on first weight send.
+    """
+
+    _target_: str = "torchrl.weight_update.SharedMemWeightSyncScheme"
+    _partial_: bool = False
+
+    policy_weights: Any = None  # dict[str, TensorDictBase] | None
+    strategy: str = "tensordict"  # "tensordict" or "state_dict"
+    auto_register: bool = True  # Enable lazy registration by default
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for shared memory weight sync scheme configurations."""
+
+
+@dataclass
+class NoWeightSyncSchemeConfig(ConfigBase):
+    """Configuration for NoWeightSyncScheme.
+
+    No-op weight synchronization scheme that disables weight synchronization entirely.
+    """
+
+    _target_: str = "torchrl.weight_update.NoWeightSyncScheme"
+    _partial_: bool = False
+
+    strategy: str = "tensordict"  # Not really used, but kept for consistency
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for no weight sync scheme configurations."""
+
+
+@dataclass
+class RayWeightSyncSchemeConfig(ConfigBase):
+    """Configuration for RayWeightSyncScheme.
+
+    Weight synchronization for Ray distributed computing. Uses Ray's object store
+    and remote calls to synchronize weights across distributed workers (Ray actors).
+    """
+
+    _target_: str = "torchrl.weight_update.RayWeightSyncScheme"
+    _partial_: bool = False
+
+    strategy: str = "tensordict"  # "tensordict" or "state_dict"
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for Ray weight sync scheme configurations."""
+
+
+@dataclass
+class RayModuleTransformSchemeConfig(ConfigBase):
+    """Configuration for RayModuleTransformScheme.
+
+    Weight synchronization for RayModuleTransform actors. This scheme is designed
+    specifically for updating models hosted within Ray actors.
+    """
+
+    _target_: str = "torchrl.weight_update.RayModuleTransformScheme"
+    _partial_: bool = False
+
+    strategy: str = "tensordict"  # "tensordict" or "state_dict"
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for Ray module transform scheme configurations."""
+
+
+@dataclass
+class RPCWeightSyncSchemeConfig(ConfigBase):
+    """Configuration for RPCWeightSyncScheme.
+
+    Weight synchronization for torch.distributed.rpc. Uses RPC calls to synchronize
+    weights across distributed workers.
+    """
+
+    _target_: str = "torchrl.weight_update.RPCWeightSyncScheme"
+    _partial_: bool = False
+
+    strategy: str = "tensordict"  # "tensordict" or "state_dict"
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for RPC weight sync scheme configurations."""
+
+
+@dataclass
+class DistributedWeightSyncSchemeConfig(ConfigBase):
+    """Configuration for DistributedWeightSyncScheme.
+
+    Weight synchronization for torch.distributed. Uses torch.distributed primitives
+    (send/recv) to synchronize weights across distributed workers.
+    """
+
+    _target_: str = "torchrl.weight_update.DistributedWeightSyncScheme"
+    _partial_: bool = False
+
+    backend: str = "gloo"  # "gloo", "nccl", etc.
+    sync: bool = True
+    strategy: str = "tensordict"  # "tensordict" or "state_dict"
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for distributed weight sync scheme configurations."""
+
+
+@dataclass
+class VLLMWeightSyncSchemeConfig(ConfigBase):
+    """Configuration for VLLMWeightSyncScheme.
+
+    Weight synchronization scheme for vLLM engines using collective communication (NCCL).
+    Broadcasts weights from a trainer to vLLM inference workers with parallelism support.
+    """
+
+    _target_: str = "torchrl.weight_update.llm.VLLMWeightSyncScheme"
+    _partial_: bool = False
+
+    master_address: str | None = None  # Defaults to "localhost"
+    master_port: int | None = None  # Auto-assigned if None
+    gpus_per_replica: int = 1  # tp_size × dp_size × pp_size
+    num_replicas: int = 1
+    strategy: str = "tensordict"  # "tensordict" or "state_dict"
+    device: Any = 0  # torch.device | str | int
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for vLLM weight sync scheme configurations."""
+
+
+@dataclass
+class VLLMDoubleBufferSyncSchemeConfig(ConfigBase):
+    """Configuration for VLLMDoubleBufferSyncScheme.
+
+    Weight synchronization scheme for vLLM using double-buffered memory-mapped storage.
+    Uses TensorDict's memory-mapping capabilities to transfer weights via filesystem.
+    """
+
+    _target_: str = "torchrl.weight_update.llm.VLLMDoubleBufferSyncScheme"
+    _partial_: bool = False
+
+    remote_addr: str | None = None  # Directory path where sender writes weights
+    local_addr: str | None = None  # Directory path where receiver reads weights
+    num_threads: int = 1  # Number of threads for memmap operations
+    strategy: str = "tensordict"  # "tensordict" or "state_dict"
+
+    def __post_init__(self) -> None:
+        """Post-initialization hook for vLLM double buffer sync scheme configurations."""
+        if self.remote_addr is None:
+            raise ValueError("remote_addr is required for VLLMDoubleBufferSyncScheme")
+        if self.local_addr is None:
+            self.local_addr = self.remote_addr