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| title: Answer Convergence Early Stopping | |
| emoji: π | |
| colorFrom: indigo | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 5.9.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Demo for EMNLP Paper "Answer Convergence as a Signal..." | |
| # π Answer Convergence as a Signal for Early Stopping in Reasoning | |
| ### [EMNLP Accepted] | [Paper (arXiv)](https://arxiv.org/abs/2506.02536) | [GitHub Code](https://github.com/launchnlp/reasoning_earlystop) | |
| **Authors:** Xin Liu, Lu Wang (University of Michigan) | |
| --- | |
| ## π‘ What is this? | |
| This Space demonstrates the core concept of our paper: **Large Language Models often internally converge on an answer long before they finish generating the full reasoning chain.** | |
| By detecting this **Answer Convergence**, we can stop the generation early, saving **40%+** of inference costs without sacrificing accuracy. | |
| ## π Key Methods | |
| We propose three strategies to detect this signal: | |
| 1. **Answer Consistency:** Unsupervised method checking if the answer stabilizes across reasoning chunks. | |
| 2. **Think Token Adjustment:** Encouraging the model to output the stop signal earlier. | |
| 3. **Learn-to-Stop:** A lightweight supervised module trained on internal activations. |