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[KDD 2026] Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models?

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# MoPPS: Model Predictive Prompt Selection

[![arXiv](https://img.shields.io/badge/arXiv-2507.04632-b31b1b.svg)](https://arxiv.org/abs/2507.04632)
[![Team Page](https://img.shields.io/badge/Team-Page-blue)](https://www.thuidm.com/)
[![GitHub](https://img.shields.io/github/stars/thu-rllab/MoPPS?style=social)](https://github.com/thu-rllab/MoPPS)

</div>

This repository contains code for **Model Predictive Prompt Selection (MoPPS)**, a framework for online predicting prompt difficulty to accelerate reinforcement learning (RL) finetuning of Large Reasoning Models.

![MoPPS framework](https://raw.githubusercontent.com/thu-rllab/MoPPS/main/assets/framework.png)

## 🔧 Installation

Ensure you have **CUDA ≥ 12.4**, then run:

```bash
bash prepare.sh
```

This script installs all required packages and dependencies.

## 📦 Dataset Preparation

We support multiple reasoning tasks. Run the following scripts to preprocess each dataset:

```bash
# Mathematics dataset
python recipe/ours/data_preprocess/math_dataset.py --local_dir='./data/math'

# Mathematics Evaluation Benchmarks from deepscaler
python recipe/ours/data_preprocess/deepscaler/deepscaler_dataset.py --local_dir='./data/deepscaler'

# Countdown-34
python recipe/ours/data_preprocess/countdown.py --local_dir='./data/countdown3to4'

# Countdown-4
python recipe/ours/data_preprocess/countdown4.py --local_dir='./data/countdown4'

# Geometry3k
python recipe/ours/data_preprocess/geo3k.py --local_dir='./data/geo3k'
```

## 📥 Download Pretrained Models

You can download models from [Hugging Face](https://huggingface.co/) as follows (example shown with DeepSeek-R1-Distill-Qwen-1.5B):

```bash
huggingface-cli download --resume-download deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --local-dir models/DeepSeek-R1-Distill-Qwen-1.5B
```

> Tip: You can change `--local-dir` to your own model path. Be sure to match it with your training configs.

## 🚀 Training

All training scripts are located in:

```
recipe/ours/scripts/
```

These include task-specific scripts for launching MoPPS and baseline methods with different backbones and datasets.

Below is an example of how to launch MoPPS training on the Countdown task with Qwen2.5-3B:

```
bash recipe/ours/scripts/countdown/cd_verl_3b_topk_noinit.sh
```

## 📥 Integration Guide
**Model Predictive Prompt Selection (MoPPS)** is a Bayesian risk-predictive framework that online estimates prompt difficulty without requiring costly LLM interactions.
Technically, MoPPS models each prompt's success rate as a latent variable, performs streaming Bayesian inference, and employs posterior sampling in a constructed multi-armed bandit machine, enabling sample efficient and adaptive prompt selection.

The main implementation is in [`mopps.py`](recipe/ours/mopps.py), featuring two key operations:

### 1️⃣ `train`: Bayesian Inference towards Prompt Success Rate

We formulate online prompt selection as a sequential decision-making problem and solve it with a dynamic Bernoulli bandit machine:

**Recursive Bayesian Update with Temporal Discounting:**

$$
\alpha^{t'}_{\tau} = \lambda \cdot \alpha^{t}_{\tau} + (1 - \lambda) \cdot \alpha_{\tau}^{0} + s^t_\tau
$$

$$
\beta^{t'}_{\tau} = \lambda \cdot \beta^{t}_{\tau} + (1 - \lambda) \cdot \beta_{\tau}^{0} + k - s^t_\tau
$$

**Implementation:**

```python
def train(self, batch_candidates_dict, y):
    if self.no_update:
        return None, None, None
    
    indices = batch_candidates_dict['index']
    for idx, s in zip(indices, y):
        idx = str(idx)
        n_rollout = self.args.actor_rollout_ref.rollout.n if self.args.actor_rollout_ref.rollout.n > 1 else 8
        
        self.alpha[idx] = (self.alpha[idx] * self.decay_ratio + 
                          self.prior_alpha * (1 - self.decay_ratio) + 
                          s * n_rollout)
        
        self.beta[idx] = (self.beta[idx] * self.decay_ratio + 
                         self.prior_beta * (1 - self.decay_ratio) + 
                         (1 - s) * n_rollout)
    
    return None, None, None
```

### 2️⃣ `sample_batch`: Active Prompt Selection

The posterior distribution's estimate of a prompt's success rate correlates strongly with ground truth. We adopt **Thompson Sampling** for its natural exploration-exploitation trade-off:

**Fast Success Rate Estimates from Approximate Posteriors:**

$$
\hat{\gamma}^t_\tau \sim \mathrm{Beta}(\alpha_\tau^{t}, \beta_\tau^{t}) \quad \forall\tau\in\mathcal{T}_{t}^{\hat{\mathcal{B}}}
$$

```python
sampled_r = np.random.beta(local_alpha, local_beta)
```

**Top-B Selection Strategy:**

$$
\mathcal{T}_t^\mathcal{B} = \text{Top-}\mathcal{B}( \{ \tau \in \mathcal{T}_{t}^{\hat{\mathcal{B}}} | -\lVert \hat{\gamma}^t_\tau - \gamma^* \rVert_2^2 \} )
$$

```python
distances = (sampled_r - target_mu) ** 2
sampled_index = np.argsort(distances)[:self.real_batch_size]
```

> 💡 **Note:** MoPPS can easily integrate with alternative selection strategies, e.g., threshold filtering.

### 🔌 Easy Integration

MoPPS can be seamlessly integrated into your RL training pipeline.

**Key Integration Points:**
- 📊 **Before rollout**: Call `sample_batch()` to select prompts
- 🔄 **After reward**: Call `train()` to update Bayesian posteriors

```python
for epoch in range(self.config.trainer.total_epochs):
    for batch_dict in self.train_dataloader:
        # Step 1: Active pre-rollout prompt selection
        if self.task_sampler is not None:
            batch_dict, acquisition_score = self.task_sampler.sample_batch(batch_dict)
        
        # ... (rollout generation & evaluation)

# Step 2: Update posterior with observed success rates
        ts_loss, ts_recon_loss, ts_kl_loss = self.task_sampler.train(
            batch_dict, 
            success_rate
        )
        # ... (RL training)
```

## 📚 Citation

If you find this work useful for your research, please cite our paper:

```bibtex
@article{qu2025can,
  title={Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models?},
  author={Qu, Yun and Wang, Qi and Mao, Yixiu and Hu, Vincent Tao and Ommer, Bj{\"o}rn and Ji, Xiangyang},
  journal={arXiv preprint arXiv:2507.04632},
  year={2025}
}
```

This work is inspired by the following prior research:
```bibtex
@article{wang2025model,
  title={Model predictive task sampling for efficient and robust adaptation},
  author={Wang, Qi and Xiao, Zehao and Mao, Yixiu and Qu, Yun and Shen, Jiayi and Lv, Yiqin and Ji, Xiangyang},
  journal={arXiv preprint arXiv:2501.11039},
  year={2025}
}
@inproceedings{qu2025fast,
  title={Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments},
  author={Qu, Yun and Wang, Cheems and Mao, Yixiu and Lv, Yiqin and Ji, Xiangyang},
  booktitle={Forty-second International Conference on Machine Learning},
  year={2025}
}
```

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