arXiv AI August 13, 2026 By Matthew M. Hong, Jesse Zhang, Anusha Nagabandi, Abhishek Gupta

TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning

arXiv:2605. 12236v2 Announce Type: replace-cross Abstract: Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narrow action distributions that lack the coverage necessary for downstream exploration.

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arXiv Machine Learning

Jun 10

MODIP: Efficient Model-Based Optimization for Diffusion Policies

arXiv:2606. 10825v1 Announce Type: new Abstract: Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC).

By Zakariae El Asri, Philippe Gratias-Quiquandon, Nicolas Thome, Olivier Sigaud

diffusion reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Jun 2

Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies

arXiv:2606. 01151v1 Announce Type: new Abstract: Behavior cloning with high-capacity generative policies achieves strong imitation performance, but is often limited by demonstration coverage and distribution shift.

By Hikmet Simsir, Ozgur S. Oguz

diffusion reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 2

SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning

arXiv:2512. 00062v2 Announce Type: replace-cross Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to collect demonstrations through human operation.

By Taewook Nam, Junmo Cho, Youngsoo Jang, Sung Ju Hwang

reinforcement-learning robotics fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 2

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.

By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters

reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Sep 17

Reinforcement Learning for Real-Time Vision-Language-Action Policies

The paper presents Real‑Time EXPO‑FT, a reinforcement learning framework that fine‑tunes large Vision‑Language‑Action models for real‑time robotic control. It separates slow, expressive action generation from fast, reactive edits, allowing a lightweight policy to adjust actions based on the latest observation. Experiments on the Kinetix benchmark and four dynamic real‑world tasks show that Real‑Time EXPO‑FT achieves superior performance, improving policy success rates from 42% to 97% with only ten minutes of online data and no human intervention.

By Perry Dong, Kuo-Han Hung, Dorsa Sadigh, Chelsea Finn

reinforcement-learning robotics fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jun 11

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.

By Sarvesh Patil, Mitsuhiko Nakamoto, Manan Agarwal, Shashwat Saxena, Jesse Zhang, Giri Anantharaman, Cleah Winston, Chaoyi Pan, Douglas Chen, Nai-Chieh Huang, Zeynep Temel, Oliver Kroemer, Sergey Levine, Abhishek Gupta, Hongkai Dai, Paarth Shah, Max Simchowitz

diffusion reinforcement-learning robotics fine-tuning benchmarks

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Related stories

MODIP: Efficient Model-Based Optimization for Diffusion Policies

Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies

SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

Reinforcement Learning for Real-Time Vision-Language-Action Policies

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies