Understanding R1-Zero-Like Training: A Critical Perspective

Zichen Liu*†, Changyu Chen*, Wenjun Li*, Penghui Qi*

Tianyu Pang, Chao Du, Wee Sun Lee, Min Lin

*Core Contributors, †Project Lead

🎉 Updates • 🔗 Links • 📖 TL;DR

💻 Usage • 🍊 Citation • 🌻 Acknowledgement

Updates

• 21/03/2025: 🎉 We release our paper, models and codebase. Our R1-Zero training is implemented with 🌾 Oat, a highly modular, research-friendly and efficient LLM RL framework.

Links

• Understanding R1-Zero-Like Training

  • 📄 Paper
  • 🤗 Models

• There May Not Be Aha Moment in R1-Zero-like Training — A Pilot Study

  • 📄 Blog
  • 💻 Code

• OAT: A research-friendly framework for LLM online alignment

  • 💻 Codebase

TL;DR

To understand R1-Zero-like training, we critically examine two core components: base models and reinforcement learning. We highlight our findings below.

On base models:

1. DeepSeek-V3-Base already exhibit "Aha moment".

2. As the popular choice for R1-Zero-like training, Qwen2.5 base models demonstrate strong reasoning capabilities even without prompt templates: the average benchmark scores improve by ~60% (compared to the traditional 4-shot prompting)!

On reinforcement learning:

3. GRPO leads to biased optimization! We propose a simple fix that improves token efficiency while maintaining reasoning performance, termed as Dr. GRPO (GRPO Done Right).

4. In R1-Zero-like training, the template and the question set perform a duet to affect the RL dynamics
   • (Left Plot) For Qwen2.5-Math-1.5B, a mismatched template (e.g., R1 template) in fact destructs the reasoning capabilities before RL reconstructing it. This makes the improvement impressive on the surface.
   • (Middle Plot) However, if a template does not deviate from the pretraining distribution too far, even a small and completely o.o.d. question set (e.g., GSM8K) could induce the reasoning ability equally well, by reinforcing correct reasoning behaviors instead of infusing new knowledge.

5. Beyond Qwen, Llama can also be RL-tuned from base models. In this case, domain-specific pretraining will improves RL ceiling.
   • (Right Plot) GRPO can even make Llama with math knowledge "Aha" by increasing the output length; however, it is likely due to its length bias, which can be removed by Dr. GRPO.

Our minimalist R1-Zero recipe:

Our analysis suggests a minimalist recipe for R1-Zero-like training:

We RL-tune Qwen2.5- Math-7B using the (unbiased) Dr. GRPO algorithm on MATH level 3-5 questions with the Qwen-Math template, and achieve state-of-the-art performance with only 27 hours compute on 8× A100 GPUs.

If you are interested in more details, please check out our paper!

Usage

Install

We recommend a clean python==3.10 environment for development.

# Install vllm & oat, the LLM RL framework we developed r1-zero training on.
pip install vllm==0.7.2 && pip install oat-llm==0.0.9

# Install this package locally to use the math grader.
git clone [email protected]:sail-sg/understand-r1-zero.git && cd understand-r1-zero
pip install -e .

Training

We implement R1-Zero training by extending Oat's Learner and Actor components. Please see train_zero_math.py for a step-by-step guide.

# Patch LD_LIBRARY_PATH to avoid dependency errors:
export LD_LIBRARY_PATH=$(python -c "import sysconfig; print(sysconfig.get_config_var('LIBDIR'))"):$LD_LIBRARY_PATH

# Run the experiment (tested on 8 x A100-40G) with Dr. GRPO:
# (change to `--critic_type grpo` for running GRPO)
python train_zero_math.py \
    --critic_type drgrpo \
    --gpus 8 \
    --enable_prefix_caching \
    --collocate \
    --vllm_sleep \
    --vllm_gpu_ratio 0.35 \
    --gradient-checkpointing \
    --flash-attn \
    --bf16 \
    --rnd-seed \
    --learning_rate 0.000001 \
    --lr_scheduler constant \
    --num_ppo_epochs 1 \
    --beta 0 \
    --oracle_type reward \
    --oracle math \
    --pretrain Qwen/Qwen2.5-Math-1.5B \
    --prompt_template r1 \
    --zero-stage 2 \
    --ref_offload \
    --prompt_data ./datasets/train/math_12k \
    --train_split train \
    --input_key problem \
    --output_key answer \
    --max-train 9999999 \
    --num_prompt_epoch 20 \
    --prompt_max_length 1024 \
    --num_samples 8 \
    --temperature 1 \
    --top_p 1 \
    --generate_max_length 3000 \
    --save_steps -1 \
    --train_batch_size 128 \
    --train_batch_size_per_device 1 \
    --mini_train_batch_size_per_device 1 \
    --rollout_batch_size 128 \
    --rollout_batch_size_per_device 16 \
    --pi_buffer_maxlen_per_device 128 \
    --eval_batch_size 200 \
    --eval_steps 16 \
    --eval_temperature 0 \
    --eval_generate_max_length 3000 \
    --eval_data ./datasets/evaluation_suite \
    --eval_input_key input \
    --use-wb \
    --wb-run-name qwen2.5-Math-1.5b-r1-zero \
    --wb_project oat-zero

Please see here for more example scripts.

Evaluation

# Evaluate our models:
python evaluate_model.py --model_name sail/Qwen2.5-Math-7B-Oat-Zero
python evaluate_model.py --model_name sail/Qwen2.5-Math-1.5B-Oat-Zero
python evaluate_model.py --model_name sail/Llama-3.2-3B-Oat-Zero --template r1

# Evaluate baseline models:
python evaluate_model.py --model_name Qwen/Qwen2.5-Math-1.5B
python evaluate_model.py --model_name Qwen/Qwen2.5-Math-7B
python evaluate_model.py --model_name hkust-nlp/Qwen-2.5-Math-7B-SimpleRL-Zero
python evaluate_model.py --model_name PRIME-RL/Eurus-2-7B-PRIME-Zero
python evaluate_model.py --model_name Open-Reasoner-Zero/Open-Reasoner-Zero-7B

Serving DeepSeek Models

We provide a script to serve DeepSeek-V3-Base and DeepSeek-R1-Zero on k8s cluster.

# prerequisites:
# 1. download the model weights
# 2. starting a k8s job with sglang docker image "lmsysorg/sglang:v0.4.3.post2-cu125"

# start the server:
bash deploy_dpsk/serving.sh <model_name> <num_nodes>

Example of API call:

from openai import OpenAI

# MASTER_ADDR is the environment variable set by the k8s job
api_base = "http://{MASTER_ADDR}:30000/v1"
api_key = "EMPTY"

client = OpenAI(
    api_key=api_key,
    base_url=api_base,
)

# send requests to the server ...

Notes:

• Your k8s container should have environment variable MASTER_ADDR and MASTER_PORT set.
• Hardware requirements: 2 x 8 x H100/800/20 for FP8 and 4 x 8 x A100/A800 for BF16.
• Please refer to sglang's official tutorial for more details.

Citation

If you find our works useful for your research, please consider citing:

• This paper:

  @article{liu2025understanding,
    title={Understanding R1-Zero-Like Training: A Critical Perspective},
    author={Zichen Liu and Changyu Chen and Wenjun Li and Penghui Qi and Tianyu Pang and Chao Du and Wee Sun Lee and Min Lin},
    journal={arXiv preprint arXiv:2503.20783},
    year={2025}
  }

• Our blog that conducted the first investigation on the "Aha moment":

  @misc{liu2025there,
    title={There May Not be Aha Moment in R1-Zero-like Training — A Pilot Study},
    author={Zichen Liu and Changyu Chen and Wenjun Li and Tianyu Pang and Chao Du and Min Lin},
    year={2025},
    howpublished={\url{https://oatllm.notion.site/oat-zero}},
    note={Notion Blog},
  }

• The training framework:

  @misc{liu2025oat,
    title={OAT: A research-friendly framework for LLM online alignment},
    author={Zichen Liu and Changyu Chen and Chao Du and Wee Sun Lee and Min Lin},
    year={2025}
    howpublished={\url{https://github.com/sail-sg/oat}},
  }

Acknowledgement

• This work is supported by Sea AI Lab for computing resources.
• The training codes are built on Oat, which employs vLLM, DeepSpeed and launchpad. We serve DeepSeek models using SGLang.
• The base models are from Qwen2.5-Math, Llama, and DeepSeek.
• We thank Qingfeng Lan for his time in thoroughly reviewing our code.