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在看完近50篇VLA+RL工作之后......

在看完近50篇VLA+RL工作之后......

发布于 2025-12-22
2 次阅读

视觉-语言-动作 + 强化学习：VLA+RL 最新研究全景（含论文、链接与代码）

随着基于大规模模仿学习的视觉-语言-动作 (VLA) 模型取得显著进展，将 VLA 与强化学习 (RL) 相结合已成为一种极具前景的新范式。该范式利用与环境的试错交互或预先采集的次优数据，进一步提升机器人的决策与执行能力。

本文对该领域的关键论文进行了分类整理，涵盖离线RL、在线RL、世界模型、推理时RL及对齐技术。

一、离线强化学习 (Offline RL)

离线 RL 预训练的 VLA 模型利用人类演示和自主收集的数据进行学习，无需实时环境交互。

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions

链接：https://arxiv.org/abs/2309.10150
代码：https://github.com/google-deepmind/q_transformer

Offline Actor-Critic Reinforcement Learning Scales to Large Models (Perceiver-Actor-Critic)

链接：https://arxiv.org/abs/2402.05546
代码：https://offline-actor-critic.github.io/

GeRM: A Generalist Robotic Model with Mixture-of-experts for Quadruped Robot

链接：https://arxiv.org/abs/2403.13358
代码：https://github.com/Improbable-AI/germ

ReinboT: Amplifying Robot Visual-Language Manipulation with Reinforcement Learning

链接：https://arxiv.org/abs/2505.07395

MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models

链接：https://arxiv.org/abs/2503.08007

CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning

链接：https://arxiv.org/pdf/2508.02219

Balancing Signal and Variance: Adaptive Offline RL Post-Training for VLA Flow Models (ARFM)

链接：https://arxiv.org/pdf/2509.04063

二、在线强化学习 (Online RL)

通过在环境中的试错交互，进一步优化 VLA 模型的性能。

1. 仿真环境内 (In Simulator)

FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning

链接：https://arxiv.org/abs/2409.16578
代码：https://github.com/flare-vla/flare

Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone (PA-RL)

链接：https://arxiv.org/abs/2412.06685
代码：https://pa-rl.github.io/

Improving Vision-Language-Action Model with Online Reinforcement Learning (iRe-VLA)

链接：https://arxiv.org/abs/2501.16664

Interactive Post-Training for Vision-Language-Action Models (RIPT-VLA)

链接：https://arxiv.org/abs/2505.17016
代码：https://github.com/OpenHelix-Team/RIPT

VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning

链接：https://arxiv.org/abs/2505.18719
代码：https://github.com/vla-rl/vla-rl

What Can RL Bring to VLA Generalization? An Empirical Study (RLVLA)

链接：https://arxiv.org/abs/2505.19789
代码：https://github.com/S-S-X/RLVLA

RFTF: Reinforcement Fine-tuning for Embodied Agents with Temporal Feedback

链接：https://arxiv.org/abs/2505.19767

SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning

链接：https://arxiv.org/pdf/2509.09674
代码：https://github.com/SimpleVLA/SimpleVLA

TGRPO: Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization

链接：https://arxiv.org/abs/2506.08440
代码：https://github.com/TGRPO/TGRPO

链接：https://arxiv.org/abs/2506.09839
代码：https://octonav.github.io/

RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models

链接：https://arxiv.org/pdf/2506.17639
代码：https://rlrc-vla.github.io/

RLinf: Reinforcement Learning Infrastructure for Agentic AI

链接：https://arxiv.org/pdf/2509.15965
代码：https://rlinf.github.io/

RLinf-VLA: A Unified and Efficient Framework for VLA+RL Training

链接：https://arxiv.org/pdf/2510.06710v1

2. 真实世界 (In Real-World)

RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning

链接：https://arxiv.org/abs/2412.09858
代码：https://rldg.github.io/

Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone

链接：https://arxiv.org/abs/2412.06685
代码：https://github.com/MaxSobolMark/PolicyAgnosticRL

Improving Vision-Language-Action Model with Online Reinforcement Learning

链接：https://arxiv.org/abs/2501.16664

ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy

链接：https://arxiv.org/abs/2502.05450
代码：https://github.com/ConRFT/ConRFT

VLAC: A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning

链接：https://github.com/InternRobotics/VLAC/blob/main/data/VLAC_EAI.pdf
代码：https://github.com/VLAC-VLA/VLAC

Self-Improving Embodied Foundation Models (Generalist)

链接：https://arxiv.org/pdf/2509.15155

三、世界模型 (World Model / Model-Based RL)

利用世界模型作为虚拟环境，实现低成本、安全的 VLA 策略后训练。

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

链接：https://arxiv.org/abs/2509.24948
代码：https://github.com/amap-cvlab/world-env

VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified Rewards in World Simulators

链接：https://arxiv.org/pdf/2510.00406
代码：https://github.com/VLA-RFT/VLA-RFT

四、推理时强化学习 (Test-Time RL)

在部署阶段利用预训练的价值函数进行实时优化或纠错。

To Err is Robotic: Rapid Value-Based Trial-and-Error during Deployment (Bellman-Guided Retrials)

链接：https://arxiv.org/abs/2406.15917
代码：https://github.com/notmahi/bellman-guided-retrials

Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance (V-GPS)

链接：https://arxiv.org/abs/2410.13816
代码：https://v-gps.github.io/

Hume: Introducing System-2 Thinking in Visual-Language-Action Model

链接：https://arxiv.org/abs/2505.21432
代码：https://github.com/Hume-VLA/Hume

VLA-Reasoner: Empowering Vision-Language-Action Models with Reasoning via Online Monte Carlo Tree Search

链接：https://arxiv.org/abs/2509.22643

五、强化学习对齐 (RL Alignment)

旨在使 VLA 策略符合人类偏好或安全约束。

GRAPE: Generalizing Robot Policy via Preference Alignment

链接：https://arxiv.org/abs/2411.19309
代码：https://github.com/GRAPE-VLA/GRAPE

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning

链接：https://arxiv.org/abs/2503.03480
代码：https://safevla.github.io/

六、其他分类 (Unclassified)

RPD: Refined Policy Distillation: From VLA Generalists to RL Experts

链接：https://arxiv.org/abs/2503.05833

总结

VLA 与 RL 的结合正处于快速爆发期。将模仿学习的大规模先验与强化学习的自进化能力相结合，是通向具身通用人工智能的关键路径。