Synthetic Data RL: Task Definition Is All You Need
Reinforcement learning (RL) is a powerful way to adapt foundation models to
specialized tasks, but its reliance on large-scale human-labeled data limits
broad adoption. We introduce Synthetic Data RL, a simple and general framework
that reinforcement fine-tunes models using only synthetic data generated from a
task definition. Our method first generates question and answer pairs from the
task definition and retrieved documents, then adapts the difficulty of the
question based on model solvability, and selects questions using the average
pass rate of the model across samples for RL training. On Qwen-2.5-7B, our
method achieves a 29.2% absolute improvement over the base model on GSM8K (+2.9
pp vs. instruction-tuned, +6.6 pp vs. Self-Instruct), 8.7% on MATH, 13.1% on
GPQA (+7.0 pp vs. SynthLLM), 8.9% on MedQA, 17.7% on CQA (law) and 13.7% on CFA
(finance). It surpasses supervised fine-tuning under the same data budget and
nearly matches RL with full human data across datasets (e.g., +17.2 pp on
GSM8K). Adding 100 human demonstrations improves the performance of GSM8K only
by 0.4 pp, showing a limited added value. By reducing human data annotation,
Synthetic Data RL enables scalable and efficient RL-based model adaptation.
Code and demos are available at https://github.com/gydpku/Data_Synthesis_RL/.
6833e65bf9ae3819ea4c568e
Yiduo Guo
6833e65bf9ae3819ea4c568f
Zhen Guo
claimed_verified
2025-05-26T08:09:28.399Z
638e4e66629b4d0a62ce1bf3
https://cdn-avatars.huggingface.co/v1/production/uploads/638e4e66629b4d0a62ce1bf3/s7uQ2qmee2CaXfjZDOovZ.jpeg
Zhen Guo
user
zguo0525
6833e65bf9ae3819ea4c5690
Chuanwei Huang
6833e65bf9ae3819ea4c5691
Zi-Ang Wang
6833e65bf9ae3819ea4c5692
Zekai Zhang
6833e65bf9ae3819ea4c5693
Haofei Yu
6833e65bf9ae3819ea4c5694
Huishuai Zhang
6833e65bf9ae3819ea4c5695
Yikang Shen
Synthetic Data RL: Task Definition Is All You Need
Reinforcement learning (RL) is a powerful way to adapt foundation models to
specialized tasks, but its reliance on large-scale human-labeled data limits
broad adoption. We introduce Synthetic Data RL, a simple and general framework
that reinforcement fine-tunes models using only synthetic data generated from a
task definition. Our method first generates question and answer pairs from the
task definition and retrieved documents, then adapts the difficulty of the
question based on model solvability, and selects questions using the average
pass rate of the model across samples for RL training. On Qwen-2.5-7B, our
method achieves a 29.2% absolute improvement over the base model on GSM8K (+2.9
pp vs. instruction-tuned, +6.6 pp vs. Self-Instruct), 8.7% on MATH, 13.1% on
GPQA (+7.0 pp vs. SynthLLM), 8.9% on MedQA, 17.7% on CQA (law) and 13.7% on CFA
(finance). It surpasses supervised fine-tuning under the same data budget and
nearly matches RL with full human data across datasets (e.g., +17.2 pp on
GSM8K). Adding 100 human demonstrations improves the performance of GSM8K only
by 0.4 pp, showing a limited added value. By reducing human data annotation,
Synthetic Data RL enables scalable and efficient RL-based model adaptation.
Code and demos are available at https://github.com/gydpku/Data_Synthesis_RL/.
Synthetic Data RL enhances foundation models through reinforcement learning using only synthetic data, achieving performance comparable to models trained with full human-labeled data.
reinforcement learning RL synthetic data reinforcement fine-tuning question and answer pairs model solvability average pass rate data budget supervised fine-tuning