Update README.md

README.md

@@ -3,17 +3,17 @@ pipeline_tag: robotics
 license: apache-2.0
 ---
 
-# $\pi_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
+# \\(\pi_{RL}\\): Online RL Fine-tuning for Flow-based Vision-Language-Action Models
 
-This repository contains artifacts related to the `$\pi_\\texttt{RL}$` framework, as introduced in the paper [$\pi_\\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models](https://huggingface.co/papers/2510.25889).
+This repository contains artifacts related to the \\(\pi_{RL}\\) framework, as introduced in the paper [π_RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models](https://huggingface.co/papers/2510.25889).
 
-The `$\pi_\\texttt{RL}$` framework is an open-source solution for training flow-based Vision-Language-Action (VLA) models in parallel simulation, addressing the challenges of applying large-scale reinforcement learning to systems with intractable action log-likelihoods from iterative denoising.
+The \\(\pi_{RL}\\) framework is an open-source solution for training flow-based Vision-Language-Action (VLA) models in parallel simulation, addressing the challenges of applying large-scale reinforcement learning to systems with intractable action log-likelihoods from iterative denoising.
 
 ## Abstract
-Vision-Language-Action (VLA) models enable robots to understand and perform complex tasks from multimodal input. Although recent work explores using reinforcement learning (RL) to automate the laborious data collection process in scaling supervised fine-tuning (SFT), applying large-scale RL to flow-based VLAs (e.g., $\pi_0$, $\pi_{0.5}$) remains challenging due to intractable action log-likelihoods from iterative denoising. We address this challenge with $\pi_{\text{RL}}$, an open-source framework for training flow-based VLAs in parallel simulation. $\pi_{\text{RL}}$ implements two RL algorithms: (1) {Flow-Noise} models the denoising process as a discrete-time MDP with a learnable noise network for exact log-likelihood computation. (2) {Flow-SDE} integrates denoising with agent-environment interaction, formulating a two-layer MDP that employs ODE-to-SDE conversion for efficient RL exploration. We evaluate $\pi_{\text{RL}}$ on LIBERO and ManiSkill benchmarks. On LIBERO, $\pi_{\text{RL}}$ boosts few-shot SFT models $\pi_0$ and $\pi_{0.5}$ from 57.6% to 97.6% and from 77.1% to 98.3%, respectively. In ManiSkill, we train $\pi_{\text{RL}}$ in 320 parallel environments, improving $\pi_0$ from 41.6% to 85.7% and $\pi_{0.5}$ from 40.0% to 84.8% across 4352 pick-and-place tasks, demonstrating scalable multitask RL under heterogeneous simulation. Overall, $\pi_{\text{RL}}$ achieves significant performance gains and stronger generalization over SFT-models, validating the effectiveness of online RL for flow-based VLAs.
+Vision-Language-Action (VLA) models enable robots to understand and perform complex tasks from multimodal input. Although recent work explores using reinforcement learning (RL) to automate the laborious data collection process in scaling supervised fine-tuning (SFT), applying large-scale RL to flow-based VLAs (e.g., \\(\pi_0\\), \\(\pi_{0.5}\\) remains challenging due to intractable action log-likelihoods from iterative denoising. We address this challenge with \\(\pi_{RL}\\), an open-source framework for training flow-based VLAs in parallel simulation. \\(\pi_{RL}\\) implements two RL algorithms: (1) {Flow-Noise} models the denoising process as a discrete-time MDP with a learnable noise network for exact log-likelihood computation. (2) {Flow-SDE} integrates denoising with agent-environment interaction, formulating a two-layer MDP that employs ODE-to-SDE conversion for efficient RL exploration. We evaluate \\(\pi_{RL}\\) on LIBERO and ManiSkill benchmarks. On LIBERO, \\(\pi_{RL}\\) boosts few-shot SFT models \\(\pi_0\\) and \\(\pi_{0.5}\\) from 57.6% to 97.6% and from 77.1% to 98.3%, respectively. In ManiSkill, we train \\(\pi_{RL}\\) in 320 parallel environments, improving \\(\pi_0\\) from 41.6% to 85.7% and \\(\pi_{0.5}\\) from 40.0% to 84.8% across 4352 pick-and-place tasks, demonstrating scalable multitask RL under heterogeneous simulation. Overall, \\(\pi_{RL}\\) achieves significant performance gains and stronger generalization over SFT-models, validating the effectiveness of online RL for flow-based VLAs.
 
 ## Further Resources
-*   **Paper**: [$\pi_\\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models](https://huggingface.co/papers/2510.25889)
+*   **Paper**: [π_RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models](https://huggingface.co/papers/2510.25889)
 *   **Code**: https://github.com/RLinf/RLinf
 *   **RLinf Project Documentation**: https://rlinf.readthedocs.io/en/latest/