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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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | # Copyright 2020-2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# /// script
# dependencies = [
# "trl[vllm,peft]",
# "math-verify",
# "latex2sympy2_extended",
# "trackio",
# "kernels",
# ]
# ///
"""
NuminaMath example: RLOO on math dataset with vLLM.
pip install math_verify num2words==0.5.14 peft trackio vllm
export TRACKIO_PROJECT="RLOO-NuminaMath-TIR"
accelerate launch --config_file examples/accelerate_configs/deepspeed_zero3.yaml examples/scripts/rloo.py
For TL;DR or other datasets with a reward model, use the generic script:
python -m trl.scripts.rloo --dataset_name trl-lib/tldr --reward_model_name_or_path ... --model_name_or_path ...
"""
import torch
from datasets import load_dataset
from peft import LoraConfig
from trl import RLOOConfig, RLOOTrainer
from trl.rewards import accuracy_reward, think_format_reward
def main():
# Dataset
train_dataset, eval_dataset = load_dataset("AI-MO/NuminaMath-TIR", split=["train[:5%]", "test[:5%]"])
SYSTEM_PROMPT = (
"A conversation between user and assistant. The user asks a question, and the assistant solves it. The "
"assistant first thinks about the reasoning process in the mind and then provides the user with the answer. "
"The reasoning process and answer are enclosed within <think></think> tags, i.e., <think>\nThis is my "
"reasoning.\n</think>\nThis is my answer."
)
def make_conversation(example):
return {
"prompt": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": example["problem"]},
],
}
train_dataset = train_dataset.map(make_conversation, remove_columns=["messages", "problem"])
eval_dataset = eval_dataset.map(make_conversation, remove_columns=["messages", "problem"])
# Training
training_args = RLOOConfig(
output_dir="Qwen3-0.6B-RLOO",
model_init_kwargs={"dtype": torch.bfloat16},
learning_rate=1e-5,
log_completions=True,
num_completions_to_print=2,
max_completion_length=1024,
gradient_accumulation_steps=2,
steps_per_generation=8,
use_vllm=True,
vllm_mode="colocate",
vllm_gpu_memory_utilization=0.5,
run_name="Qwen3-0.6B-RLOO-NuminaMath-TIR",
)
trainer = RLOOTrainer(
model="Qwen/Qwen3-0.6B",
args=training_args,
reward_funcs=[think_format_reward, accuracy_reward],
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=LoraConfig(),
)
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
trainer.push_to_hub(dataset_name="AI-MO/NuminaMath-TIR")
if __name__ == "__main__":
main()
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