# π_textttRL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models

arXiv: https://arxiv.org/abs/2510.25889 · HTML: https://mlsanity.com/paper/2510.25889/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Efficient VLA inference and adaptation, one of 13 directions in Vision-language-action models.
- Builds on 48 of its 52 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Placed in

- Vision-language-action models / [Efficient VLA inference and adaptation](https://mlsanity.com/vla/efficient-vla-inference-and-adaptation/)

## Builds on (most cited first, 30 of 48)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://mlsanity.com/paper/2502.19645/) (2025)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://mlsanity.com/paper/2501.09747/) (2025)
- [Flow-GRPO: Training Flow Matching Models via Online RL](https://mlsanity.com/paper/2505.05470/) (2025)
- [CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks](https://mlsanity.com/paper/2112.03227/) (2021)
- [SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics](https://mlsanity.com/paper/2506.01844/) (2025)
- [Evaluating Real-World Robot Manipulation Policies in Simulation](https://mlsanity.com/paper/2405.05941/) (2024)
- [TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2409.12514/) (2024)

## Built on by (most cited first, 16 of 16)

- [GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation](https://mlsanity.com/paper/2512.01801/) (2025)
- [VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models](https://mlsanity.com/paper/2512.22539/) (2025)
- [STARE-VLA: Progressive Stage-Aware Reinforcement for Fine-Tuning Vision-Language-Action Models](https://mlsanity.com/paper/2512.05107/) (2025)
- [NoiseGate: Learning Per-Latent Timestep Schedules as Information Gating in World Action Models](https://mlsanity.com/paper/2605.07794/) (2026)
- [WAM-RL: World-Action Model Reinforcement Learning with Reconstruction Rewards and Online Video SFT](https://mlsanity.com/paper/2606.17906/) (2026)
- [WCM: A World Critic Model for Vision-Language-Action Reinforcement Learning](https://mlsanity.com/paper/2607.29613/) (2026)
- [WAM-OPD: On-Policy Distillation for World Action Models](https://mlsanity.com/paper/2608.22364/) (2026)
- [Advances in GRPO for Generation Models: A Survey](https://mlsanity.com/paper/2603.06623/) (2026)
- [RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models](https://mlsanity.com/paper/2605.09410/) (2026)
- [DEFLECT: Temporal Counterfactual Preference Learning for Delay-Robust Asynchronous VLAs](https://mlsanity.com/paper/2605.19294/) (2026)
- [Potential-Guided Flow Matching for Vision-Language-Action Policy Improvement](https://mlsanity.com/paper/2606.04968/) (2026)
- [VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models](https://mlsanity.com/paper/2609.04355/) (2026)
- [HaWMPO: Hallucination-Aware World Model-based Policy Optimization for Generalist Robot Policy](https://mlsanity.com/paper/2609.09941/) (2026)
- [Prioritized Rollouts for Efficient World Model-based Vision-Language-Action Policy Optimization](https://mlsanity.com/paper/2609.22879/) (2026)
- [WAM-OPD: Sharpening World Action Models via On-Policy Distillation](https://mlsanity.com/paper/2609.34250/) (2026)
- [Many Ways to Succeed: Diversity-Driven RL Fine-Tuning for VLA Generalization](https://mlsanity.com/paper/2610.09943/) (2026)

## Abstract (arXiv)

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 RL to large-scale flow-based VLAs (eg, π0, π0.5) remains challenging due to intractable action log-likelihoods raised from flow matching. We address this challenge with π_textttRL, featuring two technical approaches: (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 π_textttRL across various benchmarks, with experiments demonstrating that RL yields significant performance improvements in both in-distribution and out-of-distribution settings.