File size: 1,733 Bytes
1fa3c6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # GRPO With Replay Buffer
This experimental trainer, trains a model with GRPO but replaces groups (and corresponding completions) that have 0 standard deviation with groups with high rewards and standard deviation that've been used to train a model in prior batches.
## Usage
```python
import torch
from trl.experimental.grpo_with_replay_buffer import GRPOWithReplayBufferConfig, GRPOWithReplayBufferTrainer
from datasets import load_dataset
dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
# Guarantee that some rewards have 0 std
def custom_reward_func(completions, **kwargs):
if torch.rand(1).item() < 0.25:
return [0] * len(completions) # simulate some None rewards
else:
return torch.rand(len(completions)).tolist()
training_args = GRPOWithReplayBufferConfig(
output_dir="./tmp",
learning_rate=1e-4,
per_device_train_batch_size=4,
num_generations=4,
max_completion_length=8,
replay_buffer_size=8,
report_to="none",
)
trainer = GRPOWithReplayBufferTrainer(
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
reward_funcs=[custom_reward_func],
args=training_args,
train_dataset=dataset,
)
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
trainer.train()
```
## GRPOWithReplayBufferTrainer
[[autodoc]] experimental.grpo_with_replay_buffer.GRPOWithReplayBufferTrainer
- train
- save_model
- push_to_hub
## GRPOWithReplayBufferConfig
[[autodoc]] experimental.grpo_with_replay_buffer.GRPOWithReplayBufferConfig
## ReplayBuffer
[[autodoc]] experimental.grpo_with_replay_buffer.ReplayBuffer
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