Update README.md

Author: hli2020

README.md

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-# χ₀
+# χ₀: Resource-Aware Robust Manipulation viaTaming Distributional Inconsistencies
 
 
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-χ₀ (**kai0**) is a resource-efficient framework for achieving production-level robustness in robotic manipulation by taming distributional inconsistencies. This repository is built on top of [openpi](https://github.com/Physical-Intelligence/openpi), the open-source models and packages for robotics published by the [Physical Intelligence team](https://www.physicalintelligence.company/).
+χ₀ (**kai0**) is a resource-efficient framework for achieving production-level robustness in robotic manipulation by taming distributional inconsistencies.
 
 χ₀ addresses the systematic distributional shift among the human demonstration distribution ($P_\text{train}$), the inductive bias learned by the policy ($Q_\text{model}$), and the test-time execution distribution ($P_\text{test}$) through three technical modules:
 
 - **[Model Arithmetic](#model-arithmetic)**: A weight-space merging strategy that combines models trained on different data subsets, efficiently capturing diverse knowledge without architectural complexity. **[Released]**
 - **[Stage Advantage](#stage-advantage-coming-soon)**: A stage-aware advantage estimator that provides stable, dense progress signals for policy training. **[Coming Soon]**
 - **[Train-Deploy Alignment](#train-deploy-alignment-coming-soon)**: Bridges the distribution gap via spatio-temporal augmentation, heuristic DAgger corrections, and temporal chunk-wise smoothing. **[Coming Soon]**
 
-χ₀ enables two sets of dual-arm robots to collaboratively orchestrate long-horizon garment manipulation — flattening, folding, and hanging — surpassing the state-of-the-art $\pi_{0.5}$ baseline by approximately 250% in success rate, with only 20 hours of data and 8 A100 GPUs.
+χ₀ enables two sets of dual-arm robots to collaboratively orchestrate long-horizon garment manipulation — flattening, folding, and hanging — surpassing the state-of-the-art $\pi_{0.5}$ baseline by approximately 250% in success rate, with `only 20 hours of data and 8 A100 GPUs`.
 
 <!-- [[Paper]](https://github.com/OpenDriveLab/kai0) [[Blog]](https://mmlab.hk/research/kai0) -->
 
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 - [Troubleshooting](#troubleshooting)
 - [Links and Community](#links-and-community)
 
-## Updates
+## Update
 
 - [Feb 10 2026] Initial release of the **Model Arithmetic** module with support for both JAX and PyTorch checkpoints (not tested thoroughly).
 - [Feb 10 2025] χ₀ paper released.