# Divergence Proximal Policy Optimization (DPPO)
## Rethinking the Trust Region in LLM Reinforcement Learning
[](https://arxiv.org/pdf/2602.04879)
[](https://github.com/sail-sg/Stable-RL)
[](https://x.com/QPHutu/status/2019435642539897303)
## ✨Getting started
1. Prepare the datasets by running [prepare_dapo_data.sh](https://github.com/verl-project/verl-recipe/blob/3490a22a0a3adeb7e4787fe70b1060b642efbae4/dapo/prepare_dapo_data.sh):
```bash
bash prepare_dapo_data.sh # This downloads the datasets to ${HOME}/verl/data by default
```
2. Prepare the model:
```bash
hf download Qwen/Qwen3-30B-A3B-Base --local-dir ${HOME}/verl/models/Qwen3-30B-A3B-Base
```
3. Run the script:
```bash
# run DPPO-Binary-KL
LOSS_MODE=dppo_kl bash examples/dppo_trainer/run_qwen30b_dppo.sh
# run DPPO-Binary-TV
LOSS_MODE=dppo_tv bash examples/dppo_trainer/run_qwen30b_dppo.sh
# run GRPO baseline
LOSS_MODE=vanilla CLIP_LOW=0.2 CLIP_HIGH=0.2 bash examples/dppo_trainer/run_qwen30b_dppo.sh
# or GRPO with clip higher
LOSS_MODE=vanilla CLIP_LOW=0.2 CLIP_HIGH=0.28 bash examples/dppo_trainer/run_qwen30b_dppo.sh
```
## 📖Introduction
Comparison of **PPO** and the proposed **DPPO** (the Binary-TV variant). **(Left)** The surrogate objective and corresponding masks for PPO and DPPO. PPO (and variants like GRPO) employs a heuristic mask based on the probability ratio. In contrast, DPPO utilizes a more principled mask based on a direct approximation of policy divergence (e.g., Total Variation), ensuring updates stay within a theoretically grounded trust region. **(Right)** Experimental results on the AIME24 using Qwen3-30B-A3B-Base. DPPO significantly outperforms GRPO baselines, achieving superior training stability and final performance even without rollout routing replay (R3).
DPPO variants achieve stable training while controlling the training-inference mismatch at a low level. In contrast, methods without a trust region (PG-IS, CISPO) or with a misspecified one (MiniRL) suffer from growing mismatch and eventual collapse.
The plots show numerical differences between a training and an inference engine for Qwen3-30B-A3B-Base with identical parameters. **(Left)** The probability ratio (used in PPO) is highly volatile for low-probability tokens. **(Right)** In contrast, the TV divergence is more stable. This highlights a key flaw of PPO's clipping mechanism: it **over-penalizes low-probability tokens**, which can slow down learning; and **under-penalizes high-probability tokens**, which can permit large, destabilizing updates.
The most frequently clipped tokens (by GRPO) are important to the reasoning task!
They are dominated by:
- numbers, like 1, 4
- mathematical symbols, like +, -, =
- reasoning and structural Words: Wait, Thus, Next
## Top-K divergence approximation
We only implement the DPPO-Binary-TV/DPPO-Binary-KL here due to their simplicity.
For the TopK divergence approximation, please refer to the [the original repo](https://github.com/sail-sg/Stable-RL) for a complete implementation.
## Citation
If you find our works useful for your research, please consider citing:
```bibtex
@article{qi2026dppo,
title={Rethinking the Trust Region in LLM Reinforcement Learning},
author={Qi, Penghui and Zhou, Xiangxin and Liu, Zichen and Pang, Tianyu and Du, Chao and Lin, Min and Lee, Wee Sun},
journal={arXiv preprint arXiv:2602.04879},
year={2026}
}
```
## 🌻Acknowledgement
We implement our reinforcement learning algorithm extending from [verl](https://github.com/volcengine/verl). We utilize [vLLM](https://github.com/vllm-project/vllm) and [sglang](https://github.com/sgl-project/sglang) for inference. Our models are trained primarily on [Qwen3 family](https://huggingface.co/collections/Qwen/qwen3). Our training data is built from [DAPO-MATH](https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k). Thanks for their great contributions!
