import gradio as gr
with gr.Blocks(css="""
#my-img img {
width: 70% !important;
display: block;
margin-left: auto;
margin-right: auto;
}
#small img {
width: 40% !important;
display: block;
margin-left: auto;
margin-right: auto;
}
""") as demo:
gr.HTML("""
""")
gr.HTML("""
Efficient Reasoning
This demo is structured in two parts, each showcasing a recent advancement in scalable and efficient reasoning with large language models:
Part 1: Elastic Reasoning focuses on budget-aware generation by explicitly separating thinking and solution stages.
Part 2: Fractured Chain-of-Thought explores sampling efficiency by fragmenting the reasoning process along multiple dimensions.
""")
gr.HTML("""
Part 1: Elastic Reasoning 🌟
""")
gr.HTML("""
""")
gr.Markdown(
"""
## 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.
""")
gr.Image("figs/framework.png", label="Framework", show_label=False, elem_id="my-img")
gr.Markdown(
"""
**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.
""")
with gr.Row():
gr.Image("figs/aime.png", label="Framework", show_label=False, elem_id="small")
gr.Image("figs/livecode.png", label="Framework", show_label=False, elem_id="small")
gr.Image("figs/codetable.png", label="Framework", show_label=False, elem_id="my-img")
gr.HTML("""
Part 2: Fractured Chain-of-Thought 🌟
""")
gr.HTML("""
""")
gr.Image("figs/frac_cot.gif", label="Framework", show_label=False, elem_id="my-img")
gr.Markdown(
"""
## Introduction
Building upon the same core insight as **Elastic Reasoning**—that correct answers can often be derived without waiting for a full chain-of-thought (CoT)—**Fractured Sampling** shifts focus to the **sampling strategy** of reasoning.
Instead of relying on complete, uninterrupted reasoning sequences, Fractured Sampling **breaks the CoT along the temporal dimension**, exploring whether it's possible to "get the right answer without thinking all the way through."
This method introduces sampling control along three key dimensions:
- **Solution Diversity (m) — sampling multiple final outputs from a single reasoning trace.
- **Trajectory Diversity (n) — sampling multiple independent reasoning traces with different seeds (vanilla CoT sampling).
- **Reasoning Depth Diversity (H) — sampling at different intermediate stages of a single reasoning trace.
Among these, the novel **reasoning depth `H`** plays a critical role: by sampling outputs at different depths of partially completed reasoning chains, the model creates multiple sets of "fragmented thoughts + solutions," which are then jointly evaluated to select the most trustworthy outcome.
""")
gr.Image("figs/frac-frame.png", label="Framework", show_label=False, elem_id="my-img")
gr.Markdown(
"""
### 🔍 Scaling Analysis of *n*, *m*, and *H* in DeepSeek-R1 Models
A detailed test-time scaling analysis on the DeepSeek-R1 series reveals the individual impact of the three sampling dimensions: `n` (number of reasoning paths), `m` (number of answers per path), and `H` (depth-wise reasoning samples).
Across multiple reasoning benchmarks, the results show that increasing **`H` — sampling across reasoning depths — yields the highest cost-effectiveness**. That is, sampling more intermediate answers along the depth of a single reasoning path leads to **greater accuracy improvements with fewer additional tokens**, compared to simply increasing the number of paths (`n`) or answers (`m`).
""")
gr.Image("figs/single.png", label="Framework", show_label=False, elem_id="my-img")
gr.Markdown(
"""
### 🔄 Joint Sampling of *n*, *m*, and *H* for Enhanced Accuracy
In practical scenarios, the sampling dimensions `n`, `m`, and `H` can be **jointly optimized** rather than tuned in isolation. By **dynamically allocating the sampling budget across these dimensions**, the model can significantly enhance its reasoning accuracy.
This joint sampling strategy leverages the complementary strengths of each dimension—diversity (`n`), redundancy (`m`), and depth-awareness (`H`)—to achieve robust performance under a fixed token budget.
""")
gr.Image("figs/combine.png", label="Framework", show_label=False, elem_id="my-img")
gr.Markdown(
"""
## 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}
}
@misc{liao2025fracturedchainofthoughtreasoning,
title={Fractured Chain-of-Thought Reasoning},
author={Baohao Liao and Hanze Dong and Yuhui Xu and Doyen Sahoo and Christof Monz and Junnan Li and Caiming Xiong},
year={2025},
eprint={2505.12992},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.12992},
}
```
""")
gr.HTML("""
""")
if __name__ == "__main__":
demo.launch()
