---
license: apache-2.0
datasets:
- agentica-org/DeepScaleR-Preview-Dataset
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
tags:
- reinforcement-learning
language:
- en
- zh
pipeline_tag: text-generation
library_name: transformers
---
SIRI: Scaling Iterative Reinforcement Learning with Interleaved Compression
📃 Paper • 📝 Wandb
---
## 🔍 Overview
**SIRI (Scaling Iterative Reinforcement Learning with Interleaved Compression)** is a reinforcement-learning–based framework designed to improve the efficiency and accuracy of **Large Reasoning Models (LRMs)**.
Traditional RL training often causes **overthinking** and long, redundant reasoning traces. Prior methods that compress outputs (length penalties, pruning, or skipping thought tokens) improve efficiency but hurt accuracy.
SIRI solves this trade-off by **iteratively alternating between compression and expansion of the reasoning budget**, controlled by a cosine length scheduler. This approach dynamically balances concise reasoning with long-horizon exploration.
---
## 🚀 Key Features
- **Interleaved Compression–Expansion**:
- *Compression phase*: forces concise, high-density reasoning by limiting rollout length.
- *Expansion phase*: restores longer rollouts to encourage exploration and planning.
- **Token Efficiency without Accuracy Loss**: Unlike previous methods, SIRI improves accuracy *while reducing average token usage*.
- **Iterative RL Training**: Built on GRPO with modifications from DAPO (clip-high/low decoupling, KL removal).
- **Generalization Across Model Sizes**: Validated on both **1.5B** and **7B** models.
---
## 📊 Benchmarks
---
## 📝 Citation
```bibtex
@misc{wen2025siriscalingiterativereinforcement,
title={SIRI: Scaling Iterative Reinforcement Learning with Interleaved Compression},
author={Haoming Wen and Yushi Bai and Juanzi Li and Jie Tang},
year={2025},
eprint={2509.25176},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2509.25176},
}
```