Backend protocol

Module: rllm.experimental.protocol

The BackendProtocol is the abstract interface that decouples the unified trainer from any specific training infrastructure. By implementing this protocol, you can plug in any model-serving, optimization, and checkpointing system while reusing the trainer’s episode generation, data transformation, rejection sampling, and logging machinery.

Class signature

class BackendProtocol(ABC, Generic[TDataset, TBatch]):
    name: str = "base_backend"
    requires_loop: bool = False

    def __init__(self, config: DictConfig, **kwargs): ...

The two type parameters let you declare your backend-specific types:

TDataset — the iterable type returned by get_dataloader (e.g. torch.utils.data.DataLoader)
TBatch — the batch type consumed by your pipeline methods (e.g. list[tinker.Datum])

What a backend provides

A backend implementation is responsible for four categories of functionality:

BackendProtocol
  |
  |-- Setup & teardown
  |     init_rollout_engine()   -- create the RolloutEngine for inference
  |     validate_config()       -- check backend-specific config
  |     get_dataloader()        -- wrap a Dataset into an iterable
  |     shutdown()              -- release resources
  |
  |-- Pipeline methods (called per batch, in order)
  |     generate_episodes()         -- stage 1: run workflows
  |     transform_to_backend_batch()-- stage 4: convert to native format
  |     process_backend_batch()     -- stage 5: forward/backward pass
  |     compute_advantages()        -- stage 6: advantage computation
  |     update_policy()             -- stage 7: optimizer step
  |
  |-- Lifecycle hooks (optional overrides)
  |     on_train_start / on_train_end
  |     on_epoch_start / on_epoch_end
  |     on_batch_start / on_batch_end
  |     on_validation_start / on_validation_end

Setup methods

`init_rollout_engine(**kwargs) -> RolloutEngine`

Called once during trainer initialization. The backend must create and return a RolloutEngine that the workflow engine will use for model inference. The trainer passes the parsed config objects as keyword arguments:

def init_rollout_engine(self, **kwargs) -> RolloutEngine:
    cf_config = kwargs.get("cf_config")           # CompactFilteringConfig
    transform_config = kwargs.get("transform_config")  # TransformConfig
    rs_config = kwargs.get("rs_config")            # RejectionSamplingConfig
    algorithm_config = kwargs.get("algorithm_config")  # AlgorithmConfig
    # ... create and return your engine

`validate_config() -> None`

Called during trainer initialization to validate backend-specific configuration. Raise or warn on invalid settings.

`get_dataloader(dataset, trainer_state) -> TDataset`

Called at the start of each epoch (training) and at each validation round. Use trainer_state.is_training to distinguish between training and validation and return the appropriate dataloader.

`shutdown() -> None`

Called when the trainer is torn down. Release GPU memory, close connections, etc.

Pipeline methods

These are called by the trainer in a fixed order during each training batch. The TrainerState object is the shared mutable context throughout a batch.

Stage 1: `generate_episodes`

generate_episodes(batch, agent_workflow_engine, is_validation) -> list[Episode]

Produce episodes by running workflows on the input batch. A typical implementation:

Prepares the batch (e.g. repeat each task group_size times for GRPO)
Sets the current model on the rollout engine
Delegates to agent_workflow_engine.execute_tasks(...)

Stage 4: `transform_to_backend_batch`

transform_to_backend_batch(trainer_state) -> TBatch

Convert the framework’s TrajectoryGroup objects into your backend-native format. This is a sync method since it is typically pure data transformation. Some backends defer transformation to process_backend_batch and return a placeholder here.

Stage 5: `process_backend_batch`

process_backend_batch(trainer_state) -> None

The main computational stage. Common operations:

Run a forward pass to compute training logprobs
Run a backward pass to compute gradients
Store results in trainer_state.backend_batch and trainer_state.extra_info

This method updates trainer_state in place (no return value).

Stage 6: `compute_advantages`

compute_advantages(trainer_state, algorithm_config) -> None

Compute per-step advantages and store them on the Step objects within each trajectory. The base class provides a default implementation using rLLM-native advantage estimators (GRPO, REINFORCE):

# Default implementation in BackendProtocol
async def compute_advantages(self, trainer_state, algorithm_config, **kwargs):
    adv_metrics = collect_reward_and_advantage_from_trajectory_groups(
        trainer_state.trajectory_groups, algorithm_config
    )
    trainer_state.metrics.update(adv_metrics)

Pre-computed advantages: If advantages are already set on the Step objects (e.g. computed during episode generation via a workflow decorator), the default implementation detects this and skips re-computation.

Stage 7: `update_policy`

update_policy(trainer_state) -> None

Run the optimizer step to update model weights. Some backends fuse this into process_backend_batch and make update_policy a no-op (see flexible stage organization below).

Lifecycle hooks

All hooks are async def methods with default no-op implementations. Override only what you need.

Training hooks

on_train_start(state)  -- called once before the first epoch
  |
  |  on_epoch_start(state)  -- called at the start of each epoch
  |    |
  |    |  on_batch_start(state)  -- called before each batch pipeline
  |    |  [... 8-stage pipeline ...]
  |    |  on_batch_end(state)    -- called after pipeline, before logging
  |    |
  |    |  (repeat for each batch)
  |    |
  |  on_epoch_end(state)  -- called at the end of each epoch
  |
  |  (repeat for each epoch)
  |
on_train_end(state)  -- called once after all epochs

Validation hooks

on_validation_start(state) -> bool  -- return False to skip validation
  [... validation loop ...]
on_validation_end(state)

Common uses for hooks

Hook	Common use
`on_train_start`	Initialize training client, load checkpoint, set initial `global_step`
`on_batch_end`	Save checkpoint, update sampling client, compute derived metrics, print metrics table
`on_train_end`	Save final checkpoint
`on_validation_start`	Toggle model to eval mode; return `False` to skip
`on_validation_end`	Toggle model back to train mode

on_batch_end runs after the pipeline but before logger.log(...). This makes it the right place to inject derived metrics (e.g. KL divergence, learning rate) into trainer_state.metrics.

Flexible stage organization

The protocol defines stages 4-7 as separate methods, but backends are free to redistribute work across them. The trainer always calls them in the same order — it is the backend’s responsibility to decide what each stage does internally.

Example: TinkerBackend’s fused mode

The TinkerBackend demonstrates this flexibility. When fuse_forward_backward_and_optim_step is enabled:

                       Default (non-fused)            Fused
                       -------------------            -----
transform_to_backend   returns [] placeholder          same
process_backend_batch  forward + backward              forward + backward + optim step
compute_advantages     stores algorithm_config         same
update_policy          optimizer step                  no-op (already done)

Both modes produce the same end result, but the fused path reduces round-trips to the training server. The trainer does not need to know which path is active — it simply calls all four methods in order.

Example: Pre-computed advantages (OPSD)

For On-Policy Self-Distillation, advantages are computed during episode generation (stage 1) via a workflow decorator. By the time compute_advantages (stage 6) runs, every Step already has its .advantage field set. The default implementation in BackendProtocol detects this and skips re-computation, collecting only metrics. This means OPSD can work with the standard TinkerBackend — no custom backend subclass is needed.

Implementing a custom backend

Subclass BackendProtocol

from rllm.experimental.protocol import BackendProtocol

class MyBackend(BackendProtocol[MyDataLoader, MyBatch]):
    name = "my_backend"

    def __init__(self, config, **kwargs):
        super().__init__(config, **kwargs)
        # ... initialize your infrastructure

Implement required methods

At minimum, implement these abstract methods:

# Setup
def init_rollout_engine(self, **kwargs) -> RolloutEngine: ...
def validate_config(self) -> None: ...
def get_dataloader(self, dataset, trainer_state) -> MyDataLoader: ...
def shutdown(self) -> None: ...

# Pipeline
async def generate_episodes(self, batch, agent_workflow_engine, is_validation=False, **kwargs) -> list[Episode]: ...
def transform_to_backend_batch(self, trainer_state, **kwargs) -> MyBatch: ...
async def process_backend_batch(self, trainer_state, **kwargs) -> None: ...
async def compute_advantages(self, trainer_state, algorithm_config, **kwargs) -> None: ...
async def update_policy(self, trainer_state, **kwargs) -> None: ...

Override lifecycle hooks as needed

async def on_train_start(self, trainer_state):
    # Load checkpoint, initialize model
    ...

async def on_batch_end(self, trainer_state):
    # Save checkpoint, update metrics
    ...

Wire it up

trainer = UnifiedTrainer(
    backend_cls=MyBackend,
    config=config,
    workflow_class=MyWorkflow,
    train_dataset=train_ds,
    val_dataset=val_ds,
)
trainer.fit()

Reference: TinkerBackend

The TinkerBackend (rllm.trainer.tinker.tinker_backend) is the primary production backend. It serves as a comprehensive reference implementation.

Setup

Method	Implementation
`init_rollout_engine`	Creates a `TinkerPolicyTrainer` and a `TinkerEngine` (rollout engine backed by a Tinker sampling server)
`validate_config`	Warns if sampling temperature/top_p deviate from 1.0
`get_dataloader`	Returns a `torch.utils.data.DataLoader` with backend-specific batch sizes
`shutdown`	Delegates to parent (no-op currently)

Pipeline

Stage	Method	Implementation
1	`generate_episodes`	Builds an interleaved batch (`N` repeats per task for GRPO grouping), sets the sampling client on the rollout engine, and calls `agent_workflow_engine.execute_tasks(...)`
4	`transform_to_backend_batch`	Returns an empty list (placeholder). The actual datum construction is deferred to stage 5
5	`process_backend_batch`	Converts trajectory groups to Tinker `Datum` objects, runs forward-backward, stores training logprobs. Optionally fuses the optimizer step
6	`compute_advantages`	Stores the `AlgorithmConfig` for use by stage 5’s datum construction (advantage computation is embedded in the forward-backward call)
7	`update_policy`	Runs the optimizer step (or no-op if fused into stage 5)

Lifecycle hooks

Hook	Implementation
`on_train_start`	Initializes the training client, loads checkpoint, sets `trainer_state.global_step` from the checkpoint’s batch index
`on_train_end`	Saves final checkpoint if not already saved
`on_batch_end`	Saves sampler checkpoint, updates `self.sampling_client`, injects `optim/lr` and KL/entropy metrics into `trainer_state.metrics`, prints metrics table
`on_epoch_start/end`	Logging only
`on_validation_start/end`	Toggles `trainer_state.is_training` flag

Key patterns

Deferred transformation. transform_to_backend_batch returns a placeholder; the real work happens in process_backend_batch. This is valid because the trainer only checks trainer_state.has_backend_batch after process_backend_batch runs.
Checkpoint-driven sampling client. The sampling_client (used by workflows for inference) is updated in on_batch_end after each checkpoint save. This ensures workflows always sample from the latest policy.
Metrics injection in on_batch_end. Since on_batch_end runs after the pipeline but before logger.log(...), it is the natural place to compute derived metrics (KL divergence, entropy, learning rate) and add them to trainer_state.metrics.

Data flow summary

  Dataset
    |
    v
  get_dataloader() --> batch
    |
    v
  generate_episodes(batch) --> list[Episode]
    |
    v
  [framework] transform to TrajectoryGroups
    |
    v
  [framework] rejection sampling & filtering
    |
    v
  transform_to_backend_batch() --> TBatch (stored in trainer_state.backend_batch)
    |
    v
  process_backend_batch()  -- forward/backward, logprobs
    |
    v
  compute_advantages()     -- advantage computation
    |
    v
  update_policy()          -- optimizer step
    |
    v
  [framework] visualization, metrics collection
    |
    v
  on_batch_end()           -- checkpoint, derived metrics
    |
    v
  logger.log(metrics)      -- wandb / tracking

Getting started

Tutorials

Unified workflow trainer

Algorithms

Monitoring

Backend protocol

Class signature

What a backend provides

Setup methods

`init_rollout_engine(**kwargs) -> RolloutEngine`

`validate_config() -> None`

`get_dataloader(dataset, trainer_state) -> TDataset`

`shutdown() -> None`

Pipeline methods

Stage 1: `generate_episodes`

Stage 4: `transform_to_backend_batch`

Stage 5: `process_backend_batch`

Stage 6: `compute_advantages`

Stage 7: `update_policy`

Lifecycle hooks

Training hooks

Validation hooks

Common uses for hooks

Flexible stage organization

Example: TinkerBackend’s fused mode

Example: Pre-computed advantages (OPSD)

Implementing a custom backend

Reference: TinkerBackend

Setup

Pipeline

Lifecycle hooks

Key patterns

Data flow summary

Getting started

Tutorials

Unified workflow trainer

Algorithms

Monitoring

​Class signature

​What a backend provides

​Setup methods

​init_rollout_engine(**kwargs) -> RolloutEngine

​validate_config() -> None

​get_dataloader(dataset, trainer_state) -> TDataset

​shutdown() -> None

​Pipeline methods

​Stage 1: generate_episodes

​Stage 4: transform_to_backend_batch

​Stage 5: process_backend_batch

​Stage 6: compute_advantages

​Stage 7: update_policy

​Lifecycle hooks

​Training hooks

​Validation hooks

​Common uses for hooks

​Flexible stage organization

​Example: TinkerBackend’s fused mode

​Example: Pre-computed advantages (OPSD)

​Implementing a custom backend

​Reference: TinkerBackend

​Setup

​Pipeline

​Lifecycle hooks

​Key patterns

​Data flow summary

Class signature

What a backend provides

Setup methods

`init_rollout_engine(**kwargs) -> RolloutEngine`

`validate_config() -> None`

`get_dataloader(dataset, trainer_state) -> TDataset`

`shutdown() -> None`

Pipeline methods

Stage 1: `generate_episodes`

Stage 4: `transform_to_backend_batch`

Stage 5: `process_backend_batch`

Stage 6: `compute_advantages`

Stage 7: `update_policy`

Lifecycle hooks

Training hooks

Validation hooks

Common uses for hooks

Flexible stage organization

Example: TinkerBackend’s fused mode

Example: Pre-computed advantages (OPSD)

Implementing a custom backend

Reference: TinkerBackend

Setup

Pipeline

Lifecycle hooks

Key patterns

Data flow summary