# Recipe: One Step Off Policy Async Trainer
**Author:** `https://github.com/meituan-search`
Last updated: 07/17/2025.
## Introduction
### Background
The current reinforcement learning training process implemented by verl is synchronous, adhering to the algorithmic
workflows of established methods like PPO, GRPO, and DAPO. In each step, training samples are generated by the latest
model, and the model is updated after training completes. While this approach aligns with off-policy reinforcement
learning and stabilizes RL training, but it suffers from severe efficiency issues.
Model updates must wait for the longest output in the generation phase to complete.
During the generation of long-tail samples, GPUs remain idle, resulting in significant underutilization.
The more severe the long-tail problem in sample generation, the lower the overall training efficiency.
For example, in DAPO 32B training, the Rollout phase accounts for approximately 70% of the total time,
and increasing resources does not reduce the Rollout duration.
> source data: https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/workspace?nw=nwusertongyuxuan361
### Solution
We have implemented the **One Step Off Async Trainer** to help alleviate this issue. This approach parallelizes the
generation and training processes, utilizing samples generated in the previous step for current training.
It also involves appropriately partitioning resources, allocating dedicated resources for generation while automatically
assigning the remainder to training. By reducing resources allocated to the generation phase, we mitigate GPU idle time
during long-tail sample generation. Throughout this process, generation and training parameters maintain a one-step off
policy.
> reference: [AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning](
> https://arxiv.org/abs/2505.24298)
Our core contributions include:
1. **Parallel Generation and Training**:
Samples for the next batch are asynchronously generated while the current batch is being trained.
2. **Resource Isolation**:
Unlike `hybrid_engine`, this method requires explicit resource allocation for rollout, with remaining resources
automatically assigned to training.
3. **NCCL Parameter Synchronization**:
Employs NCCL communication primitives for seamless parameter transfer between generation and training modules.
### Experimental Results
- **Machine Configuration**: 2 nodes with 16 H20 GPUs each
- Generation: 4 GPUs
- Training: 12 GPUs
- **Model**: Qwen2.5-Math-7B
- **Rollout Configuration**:
- **Max Response Length**: FSDP2: 20,480 tokens; Megatron: 8,192 tokens
- **Algorithm**: DAPO
- **Rollout Engine**: vLLM
| training mode | engine | step | gen | wait_prev_gen | generate_sequences | old_log_prob | update_actor | total time | acc/best@32/mean | acc/maj@32/mean |
|------------------------|---------------|------|-----|---------------|--------------------|--------------|--------------|---------------|------------------|-----------------|
| colocate sync | VLLM+FSDP2 | 749 | 321 | - | 247 | 88 | 286 | 19h18m | 0.5948 | 0.417 |
| one-step-overlap async | VLLM+FSDP2 | 520 | - | 45 | 458 | 108 | 337 | 15h34m（+23%） | 0.6165 | 0.494 |
| colocate sync | VLLM+Megatron | 699 | 207 | - | 162 | 119 | 344 | 18h21m | 0.605 | 0.4217 |
| one-step-overlap async | VLLM+Megatron | 566 | - | 59 | 501 | 120 | 347 | 13h06m (+40%) | 0.6569 | 0.4038 |
* colocate sync: step ≈ gen + old_log_prob + update_actor
* one-step-overlap async: step ≈ wait_prev_gen + old_log_prob + update_actor
> source data: https://wandb.ai/hou-zg-meituan/one-step-off-policy?nw=nwuserhouzg
## Implementation
### One Step Off Policy Async Pipline
Our implemented **One Step Off Policy Async Pipeline** integrates seamlessly into existing training logic at minimal
cost,
eliminating the need for additional sample storage management. The core mechanism uses `async_gen_next_batch`
for asynchronous rollout generation while maintaining continuous operation during epoch transitions
via `create_continuous_iterator`.
```python
# iterator generator, simplify one-step integration of the training process
def _create_continuous_iterator(self):
for epoch in range(self.config.trainer.total_epochs):
iterator = iter(self.train_dataloader)
for batch_dict in iterator:
yield epoch, batch_dict
# read next batch samples, parameters sync and launch asyn gen_seq
def _async_gen_next_batch(self, continuous_iterator):
# read train_data
try:
epoch, batch_dict = next(continuous_iterator)
except StopIteration:
return None
batch = DataProto.from_single_dict(batch_dict)
gen_batch = batch_pocess(batch)
# sync weights from actor to rollout
self.sync_rollout_weights()
# async generation
gen_batch_output = self.rollout_wg.async_generate_sequences(gen_batch)
# future encapsulated
return GenerationBatchFuture(epoch, batch, gen_batch_output)
continuous_iterator = self._create_continuous_iterator()
# run rollout first to achieve one-step-off
batch_data_future = self._async_gen_next_batch(continuous_iterator)
while batch_data_future is not None:
# wait for the gen_seq result from the previous step
batch = batch_data_future.get()
# launch the next async call to generate sequences
batch_data_future = self._async_gen_next_batch(continuous_iterator)
# compute advantages
batch = critic.compute_values(batch)
batch = reference.compute_log_prob(batch)
batch = reward.compute_reward(batch)
batch = compute_advantages(batch)
# model update
critic_metrics = critic.update_critic(batch)
actor_metrics = actor.update_actor(batch)
```
### Parameter Synchronization
The exciting point is that our nccl based weights updating for rollout model has great performance.
At most of time, the latency is under 300ms, which is negligible for RLHF.
> **sync_rollout_weights**：The time for synchronizing parameters from actor to rollout is extremely fast and can almost
> be ignored because it is implemented with nccl.
```python
class ActorRolloutRefWorker:
# actor acquires the meta-info of model parameters for parameter sync
@register(dispatch_mode=Dispatch.ONE_TO_ALL)
def get_actor_weights_info(self):
params = self._get_actor_params()
ret = []
for key, tensor in params.items():
ret.append((key, tensor.size(), tensor.dtype))
self._weights_info = ret
return ret
# rollout sets the meta-info of model parameters for parameter sync
@register(dispatch_mode=Dispatch.ONE_TO_ALL)
def set_actor_weights_info(self, weights_info):
self._weights_info = weights_info
class AsyncRayPPOTrainer(RayPPOTrainer):
def init_workers(self):
...
# rollout obtains the meta-info of model parameters from the actor for parameter sync
weights_info = self.actor_wg.get_actor_weights_info()[0]
self.rollout_wg.set_actor_weights_info(weights_info)
# Create an actor-rollout communication group for parameter sync
actor_rollout_workers = self.actor_wg.workers + self.rollout_wg.workers
collective.create_collective_group(
actor_rollout_workers,
len(actor_rollout_workers),
list(range(0, len(actor_rollout_workers))),
backend="nccl",
group_name="actor_rollout"
)
```
```python
# drive process call the actor and rollout respectively to sync parameters by nccl
def sync_rollout_weights(self):
self.actor_wg.sync_rollout_weights()
ray.get(self.rollout_wg.sync_rollout_weights())
# fsdp model parameter sync
@register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False)
def sync_rollout_weights(self):
params = self._get_actor_params() if self._is_actor else None
if self._is_rollout:
inference_model = (
self.rollout.inference_engine.llm_engine.model_executor.driver_worker.worker.model_runner.model
)
patch_vllm_moe_model_weight_loader(inference_model)
# Model parameters are broadcast tensor-by-tensor from actor to rollout
for key, shape, dtype in self._weights_info:
tensor = torch.empty(shape, dtype=dtype, device=get_torch_device().current_device())
if self._is_actor:
assert key in params
origin_data = params[key]
if hasattr(origin_data, "full_tensor"):
origin_data = origin_data.full_tensor()
if torch.distributed.get_rank() == 0:
tensor.copy_(origin_data)
from ray.util.collective import collective
collective.broadcast(tensor, src_rank=0, group_name="actor_rollout")
if self._is_rollout:
inference_model.load_weights([(key, tensor)])
```
## Usage
### FSDP2 Configuration Example
```shell
python3 -m recipe.one_step_off_policy.async_main_ppo \
--config-path=config \
--config-name='one_step_off_ppo_trainer.yaml' \
actor_rollout_ref.actor.strategy=fsdp2 \
# actor and rollout are placed separately
actor_rollout_ref.hybrid_engine=False \
# actor and rollout resource
trainer.nnodes=1 \
trainer.n_gpus_per_node=6 \
rollout.nnodes=1 \
rollout.n_gpus_per_node=2
```
### Megatron Configuration Example
```shell
python3 -m recipe.one_step_off_policy.async_main_ppo \
--config-path=config \
--config-name='one_step_off_ppo_megatron_trainer.yaml' \
actor_rollout_ref.actor.strategy=megatron \
# actor and rollout are placed separately
actor_rollout_ref.hybrid_engine=False \
# actor and rollout resource
trainer.nnodes=1 \
trainer.n_gpus_per_node=6 \
rollout.nnodes=1 \
rollout.n_gpus_per_node=2
```
### Configuration Guidelines
1. **Card Number Relationships**
Maintain either of these relationships for optimal batch distribution:
- `actor_rollout_ref.rollout.n` should be an integer divisor of:
`trainer.n_gpus_per_node * trainer.nnodes`
- `actor_rollout_ref.rollout.n * data.train_batch_size` should be evenly divisible by:
`trainer.n_gpus_per_node * trainer.nnodes`
> Rationale: Ensures training samples can be evenly distributed across training GPUs when using partial resources for
generation.
2. **Dynamic Resource Tuning**
Adjust `trainer.nnodes` `trainer.n_gpus_per_node` `rollout.nnodes` `rollout.n_gpus_per_node` based on phase
durations:
- **Ideal state**: Rollout and training phases have comparable durations
- **Diagnostic metrics**:
- Monitor `wait_prev_gen` duration
- Analyze `sequence_length` distribution
- **Adjustment strategy**:
- High `wait_prev_gen` + uniform sequence lengths → Increase rollout resources
- High `wait_prev_gen` + long-tail sequences → Optimize stopping criteria (resource increase won't help)
> **wait_prev_gen**：The time consumed waiting for the previous rollout to end (the part that is not fully
overlapped).
**Resource Configuration Strategies:**
- **Resource-constrained scenario**: Optimize resource utilization by adjusting GPU allocation ratios,
keeping the number of nodes equal to allow training and rollout to share nodes;
- Configure `trainer.nnodes = rollout.nnodes` with
`trainer.n_gpus_per_node + rollout.n_gpus_per_node = physical_gpus_per_node`. Control rollout resource
allocation by adjusting `n_gpus_per_node`.
- **Resource-abundant scenario**: Optimize performance by adjusting the number of nodes,
keeping the number of GPUs per node equal to enable independent scaling of training and rollout
parallelism.
- Configure `trainer.n_gpus_per_node = rollout.n_gpus_per_node` and control rollout resource allocation by
adjusting `trainer.nnodes` and `rollout.nnodes`to achieve optimal performance.
> **Note**: The total number of nodes required by the system is not simply `trainer.nnodes + rollout.nnodes`. The
> actual calculation depends on GPU capacity:
> - When `trainer.n_gpus_per_node + rollout.n_gpus_per_node <= physical_gpus_per_node`,
> the required node count is `max(trainer.nnodes, rollout.nnodes)`
> - When `trainer.n_gpus_per_node + rollout.n_gpus_per_node > physical_gpus_per_node`,
> the required node count is `trainer.nnodes + rollout.nnodes`
## Functional Support
| Category | Support Situation |
|--------------------|-----------------------------------------------------------------------------------------------------------------|
| train engine | FSDP2
Megatron |
| rollout engine | vLLM |
| AdvantageEstimator | GRPO
GRPO_PASSK
REINFORCE_PLUS_PLUS
RLOO
OPO
REINFORCE_PLUS_PLUS_BASELINE
GPG |
| Reward | all |