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e35fa73 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | # Copyright 2025 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.
import re
from dataclasses import dataclass, field
from datasets import load_dataset
from latex2sympy2_extended import NormalizationConfig
from math_verify import LatexExtractionConfig, parse, verify
from trl import GRPOConfig, GRPOTrainer, ModelConfig, ScriptArguments, TrlParser, get_peft_config
@dataclass
class GRPOScriptArguments(ScriptArguments):
"""
Script arguments for the GRPO training script.
Args:
reward_funcs (`list[str]`):
List of reward functions. Possible values: 'accuracy', 'format'.
"""
reward_funcs: list[str] = field(
default_factory=lambda: ["accuracy", "format"],
metadata={"help": "List of reward functions. Possible values: 'accuracy', 'format'"},
)
def accuracy_reward(completions, solution, **kwargs):
"""Reward function that checks if the completion is the same as the ground truth."""
contents = [completion[0]["content"] for completion in completions]
rewards = []
for content, sol in zip(contents, solution):
gold_parsed = parse(sol, extraction_mode="first_match", extraction_config=[LatexExtractionConfig()])
if len(gold_parsed) != 0:
# We require the answer to be provided in correct latex (no malformed operators)
answer_parsed = parse(
content,
extraction_config=[
LatexExtractionConfig(
normalization_config=NormalizationConfig(
nits=False,
malformed_operators=False,
basic_latex=True,
equations=True,
boxed=True,
units=True,
),
# Ensures that boxed is tried first
boxed_match_priority=0,
try_extract_without_anchor=False,
)
],
extraction_mode="first_match",
)
# Reward 1 if the content is the same as the ground truth, 0 otherwise
reward = float(verify(answer_parsed, gold_parsed))
else:
# If the gold solution is not parseable, we reward 1 to skip this example
reward = 1.0
print("Failed to parse gold solution: ", sol)
rewards.append(reward)
return rewards
def format_reward(completions, **kwargs):
"""Reward function that checks if the completion has a specific format."""
pattern = r"^<think>.*?</think><answer>.*?</answer>$"
completion_contents = [completion[0]["content"] for completion in completions]
matches = [re.match(pattern, content) for content in completion_contents]
return [1.0 if match else 0.0 for match in matches]
reward_funcs_registry = {
"accuracy": accuracy_reward,
"format": format_reward,
}
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> and <answer> </answer> tags, respectively, i.e., "
"<think> reasoning process here </think><answer> answer here </answer>"
)
def main(script_args, training_args, model_args):
# Get reward functions
reward_funcs = [reward_funcs_registry[func] for func in script_args.reward_funcs]
# Load the dataset
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
# Format into conversation
def make_conversation(example):
return {
"prompt": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": example["problem"]},
],
}
dataset = dataset.map(make_conversation)
dataset = dataset.remove_columns("messages")
# Initialize the GRPO trainer
trainer = GRPOTrainer(
model=model_args.model_name_or_path,
reward_funcs=reward_funcs,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
peft_config=get_peft_config(model_args),
)
# Train and push the model to the Hub
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
if __name__ == "__main__":
parser = TrlParser((GRPOScriptArguments, GRPOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
main(script_args, training_args, model_args)
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