GFPO

This feature implements the GFPO algorithm to enforce concise reasoning in the model's output generation, as proposed in the paper Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning.

Usage

To activate GFPO in [GFPOTrainer]:

set num_remains_in_group in [GFPOConfig]
define a group filter function and set it to group_filter_func in [GFPOTrainer]. group_filter_func will score the num_generations completions and The GFPOTrainer filters groups according to their scores to get top num_remains_in_group completions as a new group. Model will be trained on the filtered group.

# train_gfpo.py
from trl.experimental.gfpo import GFPOConfig, GFPOTrainer

# dummy group filter to scores the completions based on its indice in group
class GroupFilter:
    def __call__(self, group_completions, group_rewards, **kwargs):
        group_scores = []
        for completions, rewards in zip(group_completions, group_rewards):
            scores = [float(i) for i in range(len(completions))]
            group_scores.append(scores)
        return group_scores

training_args = GFPOConfig(
    output_dir="Qwen3-0.6B-GFPO",
    per_device_train_batch_size=4,
    num_remains_in_group=2,
    bf16=True,
)
trainer = GFPOTrainer(
    model="Qwen/Qwen3-0.6B",
    reward_funcs=...,
    train_dataset=...,
    args=training_args,
    group_filter_func=GroupFilter(),
)
trainer.train()

GFPOTrainer

[[autodoc]] experimental.gfpo.GFPOTrainer - train - save_model - push_to_hub

GFPOConfig

[[autodoc]] experimental.gfpo.GFPOConfig