初始化项目，由ModelHub XC社区提供模型

Model: THU-KEG/SIRI-7B-low
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- agentica-org/DeepScaleR-Preview-Dataset
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+tags:
+- reinforcement-learning
+language:
+- en
+- zh
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/64ed568ccf6118a9379a61b8/BHITqJU33sXqf-Jbytrxg.png" width="100"/>
+  <b><span style="font-size:28px">SIRI: Scaling Iterative Reinforcement Learning with Interleaved Compression</span></b>
+</p>
+
+
+
+<p align="center">
+  📃 <a href="https://arxiv.org" target="_blank">Paper</a> • 📝 <a href="https://arxiv.org" target="_blank">Wandb</a>
+</p>
+
+---
+
+## 🔍 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.
+
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/64ed568ccf6118a9379a61b8/SXow6xntEgrwhvWtzvrkE.png" alt="pareto_front" width="500"/>
+</p>
+
+---
+
+## 🚀 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
+
+
+![perf](https://cdn-uploads.huggingface.co/production/uploads/64ed568ccf6118a9379a61b8/0S2d9VZTiaoGI6_N9Vrh2.png)  
+
+
+---
+
+## 📝 Citation
+
+```bibtex