Update app.py

app.py

@@ -4,8 +4,133 @@ import gradio as gr
 with gr.Blocks() as demo:
     gr.Markdown(
     """
-    # Hello World!
-    Start typing below to see the output.
+<div align="center">
+
+# Elastic Reasoning
+<div>
+<div>
+<h3>🚀 Scalable Chain of Thoughts via Elastic Reasoning 🌟
+</div>
+</div>
+<br>
+<div align="center">
+
+[![Paper](https://img.shields.io/badge/paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/pdf/2505.05315)
+[![Hugging Face Collection](https://img.shields.io/badge/E1-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor)](https://huggingface.co/collections/Salesforce/elastic-reasoning-682b4bba108d6ea0a8bab275)
+[![Github](https://img.shields.io/badge/Elastic_Reasoning-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white)](https://github.com/SalesforceAIResearch/Elastic-Reasoning)
+
+</div>
+</div>
+
+
+## Table of Contents
+- [Introduction](#introduction)
+- [Environment Setup](#environment-setup)
+- [Training](#training)
+- [Evaluation](#evaluation)
+
+## Introduction
+We propose **Elastic Reasoning**, a novel framework for scalable chain of thoughts
+that explicitly separates reasoning into two phases—`thinking and solution`—with
+independently allocated budgets. At test time, Elastic Reasoning prioritize that
+completeness of solution segments, significantly improving reliability under tight
+resource constraints. To train models that are robust to truncated thinking, we
+introduce a lightweight `budget-constrained rollout` strategy, integrated into GRPO,
+which teaches the model to reason adaptively when the thinking process is cut
+short and generalizes effectively to unseen budget constraints without additional
+training. 
+<p align="center">
+  <img src="figs/framework.png" width="80%" />
+</p>
+
+
+**Main Takeaways**
+1. ✂️ Thinking + Solution are explicitly separated with independent budgets — boosting reliability under tight compute constraints.
+2. 🧠 Budget-Constrained Rollout: We train models to handle truncated reasoning using GRPO.
+3. 📈 Flexible scalability: Robust performance across diverse inference budgets on reasoning benchmarks like AIME and LiveCodeBench.
+4. ⚙️ Better performance with fewer tokens: Our trained model generates outputs that are 30% shorter while maintaining (or even improving) accuracy.
+
+<p align="center">
+  <img src="figs/aime.png" width="46%" />
+    <img src="figs/livecode.png" width="48%" />
+</p>
+
+<p align="center">
+  <img src="figs/codetable.png" width="90%" />
+</p>
+
+## Environment Setup
+
+
+### Installation
+```bash
+# Installing Python 3.10 Environment.
+conda create -n e1 python=3.10 -y
+conda activate e1
+
+# Installing dependencies.
+cd Elastic-Reasoning
+pip install -e ./verl
+pip install -e .
+```
+### Data
+Our raw training data is in `rllm/data/[train|test]/[code|math]/`, along with preprocessing scripts in `rllm/data/preprocess`. To convert the raw data into Parquet files for training, run:
+
+```bash
+# Download datasets from GDrive, populates rllm/data/[train|test]/[math|code]/*.json
+python scripts/data/download_datasets.py
+
+# Generate parquet files for Deepcoder/DeepscaleR in data/*.parquet
+python scripts/data/[deepcoder|deepscaler]_dataset.py
+```
+## Training
+```bash
+export MODEL_PATH="agentica-org/DeepScaleR-1.5B-Preview"
+./scripts/e1-math/e1_math_1.5b_1k_1k.sh --model $MODEL_PATH
+```
+
+## Evaluation
+
+To run our evaluation scripts, run:
+```bash
+./scripts/eval/eval_model.sh --model [CHECKPOINT_PATH] --datasets [DATASET1] [DATASET2] --output-dir [OUTPUT_DIR] --n [N_PASSES] --tp [TENSOR_PARALLEL_SIZE] --e1-mode [SEPARATE_BUDGETING] --e1-thinking-length [THINKING_LENGTH] --e1-solution-length [SOLUTION_LENGTH]
+```
+
+### Example on MATH
+```bash
+./scripts/eval/eval_model.sh --model Salesforce/E1-Math-1.5B --datasets aime math amc minerva olympiad_bench --output-dir $HOME/E1-Math-1.5B --tp 1 --n 16 --e1-mode True --e1-thinking-length 1024 --e1-solution-length 1024
+```
+### Example on LiveCodeBench
+```bash
+./scripts/eval/eval_model.sh --model Salesforce/E1-Code-14B --datasets test_livecodebench --output-dir $HOME/E1-Code-14B --tp 4 --e1-mode True --e1-thinking-length 1024 --e1-solution-length 1024
+```
+
+### Example on Codeforces
+```bash
+./scripts/eval/eval_model.sh --model Salesforce/E1-Code-14B --datasets test_codeforces --output-dir $HOME/DeepCoder-14B-Preview --tp 4 --n 8 --e1-mode True --e1-thinking-length 1024 --e1-solution-length 1024
+```
+```bash
+python scripts/deepcoder/benchmark/cf_elo_calc.py --results_path [RESULTS_JSON_PATH] --pass_n 8
+```
+
+### Unconstrained evaluation
+set `--e1-mode False` and `--max-length [Maxmum token length, e.g. 32768]`
+
+
+## Acknowledgement
+We greatly thanks [rllm](https://github.com/agentica-project/rllm) and [verl](https://github.com/volcengine/verl) for providing the awesome codebase!
+
+## Citation
+
+
+```bibtex
+@article{xu2025scalable,
+  title={Scalable Chain of Thoughts via Elastic Reasoning},
+  author={Xu, Yuhui and Dong, Hanze and Wang, Lei and Sahoo, Doyen and Li, Junnan and Xiong, Caiming},
+  journal={arXiv preprint arXiv:2505.05315},
+  year={2025}
+}
+```
     """)