Update README.md

README.md

@@ -1,29 +1,34 @@
 ---
-license: apache-2.0
 pipeline_tag: robotics
-library_name: transformers
+license: apache-2.0
+tags:
+- reinforcement-learning
+- robotic-manipulation
+- action-chunking
 ---
 
 # Mixture of Horizons in Action Chunking
 
-This repository hosts the official models and code for the paper:
-[**Mixture of Horizons in Action Chunking**](https://huggingface.co/papers/2511.19433)
+This repository hosts the official implementation of **Mixture of Horizons (MoH)**, introduced in the paper [Mixture of Horizons in Action Chunking](https://huggingface.co/papers/2511.19433).
+
+Vision-language-action (VLA) models for robotic manipulation are highly sensitive to the chosen **action chunk length**, termed **horizon** in this work. A fixed horizon presents an inherent trade-off: longer horizons offer superior global foresight but compromise fine-grained accuracy, while shorter ones provide precise local control but struggle with long-term tasks.
 
-Project Page: https://timsty1.github.io/moh/
+To address this challenge, we propose **Mixture of Horizons (MoH)**, a novel, plug-and-play strategy that fuses multiple horizons within a single policy. MoH processes action chunks in parallel segments with different horizons and integrates their outputs. This approach simultaneously leverages long-term foresight and short-term precision with minimal overhead, and enables **Dynamic Inference** through cross-horizon consensus for enhanced efficiency and robustness in complex robotic tasks.
 
-Code Repository: https://github.com/Timsty1/MixtureOfHorizons/tree/main
+-   📄 [Paper](https://huggingface.co/papers/2511.19433)
+-   📝 [Project Page](https://timsty1.github.io/moh/)
+-   💻 [Code](https://github.com/Timsty1/MixtureOfHorizons/tree/main)
 
 ## Introduction
-Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the **action chunk length** used during training, termed **horizon**. This paper proposes a **mixture of horizons (MoH)** strategy to mitigate the inherent trade-off between long-term foresight and short-term precision observed with fixed horizons. MoH rearranges action chunks into segments with different horizons, processes them in parallel with a shared action transformer, and fuses outputs. This approach allows MoH to exploit both long-term foresight and short-term precision jointly within a single model, improving performance and generalizability with minimal overhead. MoH also enables dynamic inference with adaptive horizons, achieving higher throughput while preserving superior performance.
 
 <div align="center">
   <table border="0" cellspacing="0" cellpadding="0">
     <tr>
       <td align="center" width="50%">
-        <img src="https://huggingface.co/Timsty/mixture_of_horizons/resolve/main/figure/study_of_horizons_pi0.png" alt="Trade-off Effect" width="100%">
+        <img src="https://github.com/Timsty1/MixtureOfHorizons/raw/main/figure/study_of_horizons_pi0.png" alt="Trade-off Effect" width="100%">
       </td>
       <td align="center" width="50%">
-        <img src="https://huggingface.co/Timsty/mixture_of_horizons/resolve/main/figure/intro_motivation_v2.png" alt="Mixture of Horizons" width="100%">
+        <img src="https://github.com/Timsty1/MixtureOfHorizons/raw/main/figure/intro_motivation_v2.png" alt="Mixture of Horizons" width="100%">
       </td>
     </tr>
     <tr>
@@ -37,76 +42,27 @@ Vision-language-action (VLA) models have shown remarkable capabilities in roboti
   </table>
 </div>
 
-## Quick Start
-
-### 1. Environment Setup
-
-Clone the repository and set up the conda environment:
-
-```bash
-git clone git@github.com:Timsty1/MixtureOfHorizons.git
-conda create -n moh -y python=3.10
-conda activate moh
-pip install uv
-cd MixtureOfHorizons
-uv pip install -r requirements.txt
-pip install packages/libero
-pip install packages/openpi-client
-```
-
-### 2. Modify Transformers Library
-
-This implementation requires modifying the `transformers` library to support PyTorch-type $\pi$ series models, which rely on *gemma*, *paligemma*, and *siglip*.
-
-First, locate your conda environment path:
-```bash
-conda info --base
-```
-Then, copy the provided files to the transformers library directory (replace `YOUR_CONDA_DIR` with the path found above):
-```bash
-cp -r ./src/openpi/models_pytorch/transformers_replace/* YOUR_CONDA_DIR/envs/moh/lib/python3.10/site-packages/transformers/
-```
-
-### 3. Inference with Code
-You can use our provided "eagenerate" for speedup generation just like using 'generate' from Hugging Face. Here is an example.
-
-```python
-import torch
-from eagle.model.ea_model import EaModel
-from fastchat.model import get_conversation_template
-
-# Replace with paths to your base model and EAGLE model checkpoints
-# Example: base_model_path = "lmsys/vicuna-13b-v1.3", EAGLE_model_path = "Timsty/mixture_of_horizons"
-base_model_path = "path/to/your/base_model"
-EAGLE_model_path = "path/to/your/eagle_model"
-
-model = EaModel.from_pretrained(
-    base_model_path=base_model_path,
-    ea_model_path=EAGLE_model_path,
-    torch_dtype=torch.float16,
-    low_cpu_mem_usage=True,
-    device_map="auto",
-    total_token=-1
-)
-model.eval()
-your_message="Hello"
-conv = get_conversation_template("vicuna") # Use the correct template for your base model
-conv.append_message(conv.roles[0], your_message)
-conv.append_message(conv.roles[1], None)
-prompt = conv.get_prompt()
-input_ids=model.tokenizer([prompt]).input_ids
-input_ids = torch.as_tensor(input_ids).cuda()
-output_ids=model.eagenerate(input_ids,temperature=0.5,max_new_tokens=512)
-output=model.tokenizer.decode(output_ids[0])
-print(output)
-```
-**Note:** Vicuna, LLaMA2-Chat, and LLaMA3-Instruct are both chat models. You need to use the correct chat template, otherwise it will cause abnormal output from the model and affect the performance of EAGLE.
+<br>
+
+*   **Mitigates Trade-off**: Addresses the inherent trade-off between long-term foresight and short-term precision induced by single action chunk horizons.
+*   **Plug-and-Play**: Easily integrates into existing full-attention action modules with minimal training or inference overhead.
+*   **Dynamic Inference**: Achieves higher efficiency and robustness by selecting stable actions through cross-horizon consensus.
+
+#### More results on LIBERO
+<div align="center">
+<img src="https://github.com/Timsty1/MixtureOfHorizons/raw/main/figure/libero_main.jpg" width="90%" />
+</div>
+
+## Usage
+
+For detailed instructions on environment setup, training, and evaluation, please refer to the [GitHub repository](https://github.com/Timsty1/MixtureOfHorizons/tree/main).
 
 ## ❤️ Acknowledgment
 
 We express our gratitude to [OpenPi](https://github.com/Physical-Intelligence/openpi/tree/main), [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO), and [RoboTwin](https://robotwin-platform.github.io/) for their open-source contributions.
 
 ## 📝 Citation
+
 If you feel that this paper, models, or codes are helpful, please cite our paper, thanks for your support!
 
 ```bibtex